diff --git a/docling/utils/layout_postprocessor.py b/docling/utils/layout_postprocessor.py
index 298c838c..596d65e8 100644
--- a/docling/utils/layout_postprocessor.py
+++ b/docling/utils/layout_postprocessor.py
@@ -323,8 +323,8 @@ def _process_special_clusters(self) -> List[Cluster]:
                 contained = self._sort_clusters(contained)
                 special.children = contained
 
-                # Adjust bbox only for wrapper types
-                if special.label in self.WRAPPER_TYPES:
+                # Adjust bbox only for Form and Key-Value-Region, not Table or Picture
+                if special.label in [DocItemLabel.FORM, DocItemLabel.KEY_VALUE_REGION]:
                     special.bbox = BoundingBox(
                         l=min(c.bbox.l for c in contained),
                         t=min(c.bbox.t for c in contained),
@@ -332,12 +332,12 @@ def _process_special_clusters(self) -> List[Cluster]:
                         b=max(c.bbox.b for c in contained),
                     )
 
-                    # Collect all cells from children
-                    all_cells = []
-                    for child in contained:
-                        all_cells.extend(child.cells)
-                    special.cells = self._deduplicate_cells(all_cells)
-                    special.cells = self._sort_cells(special.cells)
+                # Collect all cells from children
+                all_cells = []
+                for child in contained:
+                    all_cells.extend(child.cells)
+                special.cells = self._deduplicate_cells(all_cells)
+                special.cells = self._sort_cells(special.cells)
 
         picture_clusters = [
             c for c in special_clusters if c.label == DocItemLabel.PICTURE
diff --git a/tests/data/groundtruth/docling_v1/2203.01017v2.doctags.txt b/tests/data/groundtruth/docling_v1/2203.01017v2.doctags.txt
index 08d617e2..712c1118 100644
--- a/tests/data/groundtruth/docling_v1/2203.01017v2.doctags.txt
+++ b/tests/data/groundtruth/docling_v1/2203.01017v2.doctags.txt
@@ -6,34 +6,33 @@
 <subtitle-level-1><location><page_1><loc_52><loc_71><loc_67><loc_72></location>a. Picture of a table:</subtitle-level-1>
 <subtitle-level-1><location><page_1><loc_8><loc_30><loc_21><loc_32></location>1. Introduction</subtitle-level-1>
 <paragraph><location><page_1><loc_8><loc_10><loc_47><loc_29></location>The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.</paragraph>
+<figure>
+<location><page_1><loc_52><loc_62><loc_88><loc_71></location>
+</figure>
 <caption><location><page_1><loc_8><loc_35><loc_47><loc_70></location>Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.</caption>
 <table>
-<location><page_1><loc_54><loc_65><loc_75><loc_70></location>
+<location><page_1><loc_52><loc_62><loc_88><loc_71></location>
 <caption>Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.</caption>
-<row_0><col_0><body></col_0><col_1><col_header>3</col_1></row_0>
-<row_1><col_0><body>2</col_0><col_1><body></col_1></row_1>
+<row_0><col_0><col_header>3</col_0><col_1><col_header>1</col_1></row_0>
 </table>
-<figure>
-<location><page_1><loc_52><loc_62><loc_88><loc_71></location>
-</figure>
 <paragraph><location><page_1><loc_52><loc_58><loc_79><loc_60></location>- b. Red-annotation of bounding boxes, Blue-predictions by TableFormer</paragraph>
 <figure>
 <location><page_1><loc_51><loc_48><loc_88><loc_57></location>
 </figure>
 <paragraph><location><page_1><loc_52><loc_46><loc_80><loc_47></location>- c. Structure predicted by TableFormer:</paragraph>
-<table>
-<location><page_1><loc_52><loc_38><loc_81><loc_45></location>
-<row_0><col_0><body>0</col_0><col_1><body>1 2</col_1><col_2><body>1</col_2></row_0>
-<row_1><col_0><body>3 4</col_0><col_1><body>5 3</col_1><col_2><body>6</col_2></row_1>
-<row_2><col_0><body>9</col_0><col_1><body>10</col_1><col_2><body>11</col_2></row_2>
-<row_3><col_0><body>8 13 2</col_0><col_1><body>14</col_1><col_2><body>15</col_2></row_3>
-<row_4><col_0><body>17</col_0><col_1><body>18</col_1><col_2><body>19</col_2></row_4>
-</table>
-<caption><location><page_1><loc_50><loc_29><loc_89><loc_35></location>Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.</caption>
 <figure>
 <location><page_1><loc_52><loc_37><loc_88><loc_45></location>
-<caption>Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.</caption>
 </figure>
+<caption><location><page_1><loc_50><loc_29><loc_89><loc_35></location>Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.</caption>
+<table>
+<location><page_1><loc_52><loc_37><loc_88><loc_45></location>
+<caption>Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.</caption>
+<row_0><col_0><col_header>0</col_0><col_1><col_header>1</col_1><col_2><col_header>1</col_2><col_3><col_header>2 1</col_3><col_4><col_header>2 1</col_4><col_5><body></col_5></row_0>
+<row_1><col_0><body>3</col_0><col_1><body>4</col_1><col_2><body>5 3</col_2><col_3><body>6</col_3><col_4><body>7</col_4><col_5><body></col_5></row_1>
+<row_2><col_0><body>8</col_0><col_1><body>9</col_1><col_2><body>10</col_2><col_3><body>11</col_3><col_4><body>12</col_4><col_5><body>2</col_5></row_2>
+<row_3><col_0><body></col_0><col_1><body>13</col_1><col_2><body>14</col_2><col_3><body>15</col_3><col_4><body>16</col_4><col_5><body>2</col_5></row_3>
+<row_4><col_0><body></col_0><col_1><body>17</col_1><col_2><body>18</col_2><col_3><body>19</col_3><col_4><body>20</col_4><col_5><body>2</col_5></row_4>
+</table>
 <paragraph><location><page_1><loc_50><loc_16><loc_89><loc_26></location>Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.</paragraph>
 <paragraph><location><page_1><loc_50><loc_10><loc_89><loc_16></location>The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be</paragraph>
 <paragraph><location><page_2><loc_8><loc_88><loc_47><loc_91></location>considered as a solved problem, given enough ground-truth data to train on.</paragraph>
@@ -71,7 +70,7 @@
 <paragraph><location><page_4><loc_8><loc_10><loc_47><loc_20></location>In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third</paragraph>
 <caption><location><page_4><loc_50><loc_72><loc_89><loc_79></location>Table 1: Both "Combined-Tabnet" and "CombinedTabnet" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.</caption>
 <table>
-<location><page_4><loc_52><loc_80><loc_88><loc_91></location>
+<location><page_4><loc_51><loc_80><loc_89><loc_91></location>
 <caption>Table 1: Both "Combined-Tabnet" and "CombinedTabnet" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.</caption>
 <row_0><col_0><body></col_0><col_1><col_header>Tags</col_1><col_2><col_header>Bbox</col_2><col_3><col_header>Size</col_3><col_4><col_header>Format</col_4></row_0>
 <row_1><col_0><row_header>PubTabNet</col_0><col_1><body>3</col_1><col_2><body>3</col_2><col_3><body>509k</col_3><col_4><body>PNG</col_4></row_1>
@@ -126,7 +125,7 @@
 <paragraph><location><page_7><loc_8><loc_50><loc_47><loc_69></location>Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.</paragraph>
 <caption><location><page_7><loc_8><loc_23><loc_47><loc_25></location>Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).</caption>
 <table>
-<location><page_7><loc_11><loc_27><loc_46><loc_48></location>
+<location><page_7><loc_9><loc_26><loc_46><loc_48></location>
 <caption>Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).</caption>
 <row_0><col_0><col_header>Model</col_0><col_1><col_header>Dataset</col_1><col_2><col_header>Simple</col_2><col_3><col_header>TEDS Complex</col_3><col_4><col_header>All</col_4></row_0>
 <row_1><col_0><row_header>EDD</col_0><col_1><body>PTN</col_1><col_2><body>91.1</col_2><col_3><body>88.7</col_3><col_4><body>89.9</col_4></row_1>
@@ -145,7 +144,7 @@
 <paragraph><location><page_7><loc_50><loc_71><loc_89><loc_91></location>our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.</paragraph>
 <caption><location><page_7><loc_50><loc_57><loc_89><loc_60></location>Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.</caption>
 <table>
-<location><page_7><loc_53><loc_62><loc_86><loc_68></location>
+<location><page_7><loc_50><loc_62><loc_87><loc_69></location>
 <caption>Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.</caption>
 <row_0><col_0><col_header>Model</col_0><col_1><col_header>Dataset</col_1><col_2><col_header>mAP</col_2><col_3><col_header>mAP (PP)</col_3></row_0>
 <row_1><col_0><body>EDD+BBox</col_0><col_1><body>PubTabNet</col_1><col_2><body>79.2</col_2><col_3><body>82.7</col_3></row_1>
@@ -155,9 +154,9 @@
 <paragraph><location><page_7><loc_50><loc_34><loc_89><loc_54></location>Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations.</paragraph>
 <caption><location><page_7><loc_50><loc_13><loc_89><loc_17></location>Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.</caption>
 <table>
-<location><page_7><loc_56><loc_19><loc_84><loc_31></location>
+<location><page_7><loc_54><loc_19><loc_85><loc_32></location>
 <caption>Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.</caption>
-<row_0><col_0><col_header>Model</col_0><col_1><col_header>Simple</col_1><col_2><col_header>TEDS Complex</col_2><col_3><col_header>All</col_3></row_0>
+<row_0><col_0><body>Model</col_0><col_1><col_header>Simple</col_1><col_2><col_header>TEDS Complex</col_2><col_3><col_header>All</col_3></row_0>
 <row_1><col_0><row_header>Tabula</col_0><col_1><body>78.0</col_1><col_2><body>57.8</col_2><col_3><body>67.9</col_3></row_1>
 <row_2><col_0><row_header>Traprange</col_0><col_1><body>60.8</col_1><col_2><body>49.9</col_2><col_3><body>55.4</col_3></row_2>
 <row_3><col_0><row_header>Camelot</col_0><col_1><body>80.0</col_1><col_2><body>66.0</col_2><col_3><body>73.0</col_3></row_3>
@@ -178,9 +177,9 @@
 <caption>b. Structure predicted by TableFormer, with superimposed matched PDF cell text:</caption>
 </figure>
 <table>
-<location><page_8><loc_9><loc_63><loc_48><loc_72></location>
+<location><page_8><loc_9><loc_63><loc_49><loc_72></location>
 <row_0><col_0><body></col_0><col_1><body></col_1><col_2><col_header>論文ファイル</col_2><col_3><col_header>論文ファイル</col_3><col_4><col_header>参考文献</col_4><col_5><col_header>参考文献</col_5></row_0>
-<row_1><col_0><body>出典</col_0><col_1><col_header>ファイル 数</col_1><col_2><col_header>英語</col_2><col_3><col_header>日本語</col_3><col_4><col_header>英語</col_4><col_5><col_header>日本語</col_5></row_1>
+<row_1><col_0><col_header>出典</col_0><col_1><col_header>ファイル 数</col_1><col_2><col_header>英語</col_2><col_3><col_header>日本語</col_3><col_4><col_header>英語</col_4><col_5><col_header>日本語</col_5></row_1>
 <row_2><col_0><row_header>Association for Computational Linguistics(ACL2003)</col_0><col_1><body>65</col_1><col_2><body>65</col_2><col_3><body>0</col_3><col_4><body>150</col_4><col_5><body>0</col_5></row_2>
 <row_3><col_0><row_header>Computational Linguistics(COLING2002)</col_0><col_1><body>140</col_1><col_2><body>140</col_2><col_3><body>0</col_3><col_4><body>150</col_4><col_5><body>0</col_5></row_3>
 <row_4><col_0><row_header>電気情報通信学会 2003 年総合大会</col_0><col_1><body>150</col_1><col_2><body>8</col_2><col_3><body>142</col_3><col_4><body>223</col_4><col_5><body>147</col_5></row_4>
@@ -192,7 +191,7 @@
 </table>
 <caption><location><page_8><loc_62><loc_62><loc_90><loc_63></location>Text is aligned to match original for ease of viewing</caption>
 <table>
-<location><page_8><loc_50><loc_64><loc_89><loc_72></location>
+<location><page_8><loc_50><loc_64><loc_90><loc_72></location>
 <caption>Text is aligned to match original for ease of viewing</caption>
 <row_0><col_0><body></col_0><col_1><col_header>Shares (in millions)</col_1><col_2><col_header>Shares (in millions)</col_2><col_3><col_header>Weighted Average Grant Date Fair Value</col_3><col_4><col_header>Weighted Average Grant Date Fair Value</col_4></row_0>
 <row_1><col_0><body></col_0><col_1><col_header>RS U s</col_1><col_2><col_header>PSUs</col_2><col_3><col_header>RSUs</col_3><col_4><col_header>PSUs</col_4></row_1>
diff --git a/tests/data/groundtruth/docling_v1/2203.01017v2.json b/tests/data/groundtruth/docling_v1/2203.01017v2.json
index 77c2f1bd..3488a8f8 100644
--- a/tests/data/groundtruth/docling_v1/2203.01017v2.json
+++ b/tests/data/groundtruth/docling_v1/2203.01017v2.json
@@ -1 +1 @@
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Picture of a table:", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.111976623535156, 241.30950927734375, 126.94803619384766, 252.05723571777344], "page": 1, "span": [0, 15], "__ref_s3_data": null}], "text": "1. Introduction", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.111976623535156, 78.84822082519531, 286.3650817871094, 231.216796875], "page": 1, "span": [0, 712], "__ref_s3_data": null}], "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.111976623535156, 279.00335693359375, 286.3651123046875, 550.6049194335938], "page": 1, "span": [0, 1320], "__ref_s3_data": null}], "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [315.5670166015625, 458.7572021484375, 486.4019470214844, 478.3052062988281], "page": 1, "span": [0, 68], "__ref_s3_data": null}], "text": "- b. Red-annotation of bounding boxes, Blue-predictions by TableFormer", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"prov": [{"bbox": [315.5670166015625, 363.0691833496094, 491.1912536621094, 371.81719970703125], "page": 1, "span": [0, 38], "__ref_s3_data": null}], "text": "- c. Structure predicted by TableFormer:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [308.86199951171875, 232.7270965576172, 545.1151733398438, 277.4996337890625], "page": 1, "span": [0, 220], "__ref_s3_data": null}], "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"prov": [{"bbox": [308.86199951171875, 126.95307159423828, 545.1151733398438, 207.59063720703125], "page": 1, "span": [0, 363], "__ref_s3_data": null}], "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 78.84806823730469, 545.1151123046875, 123.61963653564453], "page": 1, "span": [0, 229], "__ref_s3_data": null}], "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1210021972656, 48.9600715637207, 300.102294921875, 57.866634368896484], "page": 1, "span": [0, 1], "__ref_s3_data": null}], "text": "1", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [50.11199951171875, 695.9300537109375, 286.36505126953125, 716.7916259765625], "page": 2, "span": [0, 75], "__ref_s3_data": null}], "text": "considered as a solved problem, given enough ground-truth data to train on.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 563.9699096679688, 286.3651428222656, 692.4285888671875], "page": 2, "span": [0, 626], "__ref_s3_data": null}], "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 420.054931640625, 286.3651123046875, 560.4684448242188], "page": 2, "span": [0, 643], "__ref_s3_data": null}], "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 359.8269958496094, 286.3665771484375, 416.5534973144531], "page": 2, "span": [0, 242], "__ref_s3_data": null}], "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [61.56901550292969, 302.6770324707031, 286.3648986816406, 347.568115234375], "page": 2, "span": [0, 166], "__ref_s3_data": null}], "text": "- \u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.56901550292969, 245.0740509033203, 286.3648986816406, 289.9661560058594], "page": 2, "span": [0, 181], "__ref_s3_data": null}], "text": "- \u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.569000244140625, 199.4270477294922, 286.36492919921875, 232.3631591796875], "page": 2, "span": [0, 106], "__ref_s3_data": null}], "text": "- \u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.569007873535156, 153.779052734375, 286.3650817871094, 186.5966033935547], "page": 2, "span": [0, 131], "__ref_s3_data": null}], "text": "- \u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11200714111328, 96.63004302978516, 286.3651123046875, 141.401611328125], "page": 2, "span": [0, 231], "__ref_s3_data": null}], "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [60.97100067138672, 79.27845764160156, 183.7305450439453, 86.40372467041016], "page": 2, "span": [0, 40], "__ref_s3_data": null}], "text": "$^{1}$https://github.com/IBM/SynthTabNet", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [295.1210021972656, 48.96015548706055, 300.102294921875, 57.86671829223633], "page": 2, "span": [0, 1], "__ref_s3_data": null}], "text": "2", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.86199951171875, 683.9750366210938, 545.1151123046875, 716.7916259765625], "page": 2, "span": [0, 166], "__ref_s3_data": null}], "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 659.5203247070312, 498.28021240234375, 670.26806640625], "page": 2, "span": [0, 37], "__ref_s3_data": null}], "text": "2. Previous work and State of the Art", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.86199951171875, 461.54498291015625, 545.1151733398438, 649.7786254882812], "page": 2, "span": [0, 901], "__ref_s3_data": null}], "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 341.9270935058594, 545.115234375, 458.4305419921875], "page": 2, "span": [0, 552], "__ref_s3_data": null}], "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619689941406, 78.84815216064453, 545.1168823242188, 338.9322204589844], "page": 2, "span": [0, 1262], "__ref_s3_data": null}], "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \"image-encoder \u2192 text-decoder\" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \"image-encoder \u2192 dual decoder\" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 707.8850708007812, 250.15101623535156, 716.7916259765625], "page": 3, "span": [0, 51], "__ref_s3_data": null}], "text": "tag-decoder which is constrained to the table-tags.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 516.5458984375, 286.3651428222656, 704.7806396484375], "page": 3, "span": [0, 864], "__ref_s3_data": null}], "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199188232422, 301.297119140625, 286.3651123046875, 513.56103515625], "page": 3, "span": [0, 1007], "__ref_s3_data": null}], "text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 169.733154296875, 286.36627197265625, 298.3112487792969], "page": 3, "span": [0, 619], "__ref_s3_data": null}], "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 145.30743408203125, 105.22545623779297, 156.05516052246094], "page": 3, "span": [0, 11], "__ref_s3_data": null}], "text": "3. Datasets", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 78.84813690185547, 286.3650817871094, 135.57470703125], "page": 3, "span": [0, 281], "__ref_s3_data": null}], "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1210021972656, 48.96023941040039, 300.102294921875, 57.86680221557617], "page": 3, "span": [0, 1], "__ref_s3_data": null}], "text": "3", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.86199951171875, 503.3020935058594, 545.1151123046875, 524.1636352539062], "page": 3, "span": [0, 104], "__ref_s3_data": null}], "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [308.86199951171875, 465.6200866699219, 437.27001953125, 474.5266418457031], "page": 3, "span": [0, 33], "__ref_s3_data": null}], "text": "balance in the previous datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 164.6382598876953, 545.1151733398438, 460.4686279296875], "page": 3, "span": [0, 1400], "__ref_s3_data": null}], "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \"simple\" when it does not contain row spans or column spans, otherwise it is \"complex\". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 78.84823608398438, 545.1151123046875, 159.48580932617188], "page": 3, "span": [0, 406], "__ref_s3_data": null}], "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 695.9300537109375, 286.3651123046875, 716.7916259765625], "page": 4, "span": [0, 93], "__ref_s3_data": null}], "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 478.8949279785156, 286.3651428222656, 691.0396118164062], "page": 4, "span": [0, 983], "__ref_s3_data": null}], "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 357.50103759765625, 286.3651123046875, 474.0044860839844], "page": 4, "span": [0, 571], "__ref_s3_data": null}], "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 164.37611389160156, 286.3665466308594, 352.610595703125], "page": 4, "span": [0, 941], "__ref_s3_data": null}], "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11201477050781, 78.84810638427734, 286.3651123046875, 159.4856719970703], "page": 4, "span": [0, 405], "__ref_s3_data": null}], "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1209716796875, 48.96018600463867, 300.1022644042969, 57.86674880981445], "page": 4, "span": [0, 1], "__ref_s3_data": null}], "text": "4", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.86199951171875, 567.6110229492188, 545.1150512695312, 624.338623046875], "page": 4, "span": [0, 267], "__ref_s3_data": null}], "text": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"prov": [{"bbox": [308.86199951171875, 497.6080322265625, 545.1151733398438, 542.3795776367188], "page": 4, "span": [0, 210], "__ref_s3_data": null}], "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.8169860839844, 485.321044921875, 542.7439575195312, 494.22760009765625], "page": 4, "span": [0, 57], "__ref_s3_data": null}], "text": "Tab. 1 summarizes the various attributes of the datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 460.0683288574219, 444.9360656738281, 470.8160400390625], "page": 4, "span": [0, 24], "__ref_s3_data": null}], "text": "4. The TableFormer model", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.86199951171875, 345.5131530761719, 545.115234375, 450.06060791015625], "page": 4, "span": [0, 504], "__ref_s3_data": null}], "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 324.45367431640625, 420.16058349609375, 334.30572509765625], "page": 4, "span": [0, 24], "__ref_s3_data": null}], "text": "4.1. Model architecture.", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.8619689941406, 127.00019073486328, 545.11572265625, 315.2347106933594], "page": 4, "span": [0, 907], "__ref_s3_data": null}], "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619689941406, 78.84818267822266, 545.1151123046875, 123.73930358886719], "page": 4, "span": [0, 223], "__ref_s3_data": null}], "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199188232422, 567.0330810546875, 545.1084594726562, 588.0142211914062], "page": 5, "span": [0, 212], "__ref_s3_data": null}], "text": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [50.11199951171875, 111.72905731201172, 286.365966796875, 264.2171936035156], "page": 5, "span": [0, 745], "__ref_s3_data": null}], "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [308.86199951171875, 497.69305419921875, 545.1150512695312, 542.465576171875], "page": 5, "span": [0, 227], "__ref_s3_data": null}], "text": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619384765625, 378.0381774902344, 545.1151123046875, 494.6601867675781], "page": 5, "span": [0, 563], "__ref_s3_data": null}], "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \"Scene Understanding\", \"Image Captioning\"), something which we relate to the simplicity of table images.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619689941406, 246.4272918701172, 545.1151123046875, 374.8857421875], "page": 5, "span": [0, 592], "__ref_s3_data": null}], "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619384765625, 138.727294921875, 545.1151123046875, 243.39540100097656], "page": 5, "span": [0, 483], "__ref_s3_data": null}], "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619384765625, 78.84827423095703, 545.1150512695312, 135.57484436035156], "page": 5, "span": [0, 286], "__ref_s3_data": null}], "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1209411621094, 48.96027755737305, 300.10223388671875, 57.86684036254883], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "5", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [50.11199951171875, 636.1539916992188, 286.3651428222656, 716.7916259765625], "page": 6, "span": [0, 380], "__ref_s3_data": null}], "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 551.7369384765625, 286.3651123046875, 632.3755493164062], "page": 6, "span": [0, 371], "__ref_s3_data": null}], "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 347.76910400390625, 286.36572265625, 548.0780639648438], "page": 6, "span": [0, 985], "__ref_s3_data": null}], "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112022399902344, 323.12811279296875, 286.364990234375, 343.9896545410156], "page": 6, "span": [0, 67], "__ref_s3_data": null}], "text": "The loss used to train the TableFormer can be defined as following:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [124.33001708984375, 274.92828369140625, 286.3624267578125, 298.71905517578125], "page": 6, "span": [0, 84], "__ref_s3_data": null}], "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 - \u03bb ) l$_{box}$ (1)", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.112030029296875, 251.78411865234375, 281.596923828125, 261.4079895019531], "page": 6, "span": [0, 76], "__ref_s3_data": null}], "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11204528808594, 225.33538818359375, 171.9833526611328, 236.08311462402344], "page": 6, "span": [0, 23], "__ref_s3_data": null}], "text": "5. Experimental Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11204528808594, 205.8836212158203, 179.17501831054688, 215.7356719970703], "page": 6, "span": [0, 27], "__ref_s3_data": null}], "text": "5.1. Implementation Details", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11204528808594, 151.4931182861328, 286.36517333984375, 196.2656707763672], "page": 6, "span": [0, 207], "__ref_s3_data": null}], "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [91.66104888916016, 113.60411834716797, 286.3624572753906, 138.1719970703125], "page": 6, "span": [0, 77], "__ref_s3_data": null}], "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.112060546875, 78.8481216430664, 286.3651428222656, 99.70968627929688], "page": 6, "span": [0, 117], "__ref_s3_data": null}], "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.12103271484375, 48.96010971069336, 300.1023254394531, 57.86667251586914], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "6", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.862060546875, 683.97509765625, 545.115234375, 716.7916870117188], "page": 6, "span": [0, 156], "__ref_s3_data": null}], "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862060546875, 463.6259460449219, 545.1152954101562, 675.7706298828125], "page": 6, "span": [0, 1024], "__ref_s3_data": null}], "text": "The Transformer Encoder consists of two \"Transformer Encoder Layers\", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \"Transformer Decoder Layers\" with similar input and output dimensions as the \"Transformer Encoder Layers\". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 362.83001708984375, 545.1151733398438, 455.4224853515625], "page": 6, "span": [0, 419], "__ref_s3_data": null}], "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 238.12310791015625, 545.115234375, 354.6255798339844], "page": 6, "span": [0, 528], "__ref_s3_data": null}], "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 202.5936279296875, 397.44281005859375, 212.4456787109375], "page": 6, "span": [0, 19], "__ref_s3_data": null}], "text": "5.2. Generalization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.8620300292969, 119.86811065673828, 545.1151733398438, 188.55067443847656], "page": 6, "span": [0, 299], "__ref_s3_data": null}], "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 78.84710693359375, 545.115234375, 111.6646728515625], "page": 6, "span": [0, 155], "__ref_s3_data": null}], "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 707.74658203125, 167.89825439453125, 717.5986328125], "page": 7, "span": [0, 25], "__ref_s3_data": null}], "text": "5.3. Datasets and Metrics", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11199951171875, 653.8770141601562, 286.3651123046875, 698.6495971679688], "page": 7, "span": [0, 192], "__ref_s3_data": null}], "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [86.218994140625, 619.26123046875, 286.3623962402344, 641.6820068359375], "page": 7, "span": [0, 99], "__ref_s3_data": null}], "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 - EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.11198425292969, 578.02099609375, 286.36285400390625, 610.9970092773438], "page": 7, "span": [0, 162], "__ref_s3_data": null}], "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 557.3284912109375, 170.45169067382812, 567.1805419921875], "page": 7, "span": [0, 26], "__ref_s3_data": null}], "text": "5.4. Quantitative Analysis", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11199951171875, 395.862060546875, 286.3651428222656, 548.35009765625], "page": 7, "span": [0, 723], "__ref_s3_data": null}], "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 178.705078125, 286.3651123046875, 199.56663513183594], "page": 7, "span": [0, 101], "__ref_s3_data": null}], "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/3"}, {"prov": [{"bbox": [50.11199951171875, 166.7500762939453, 261.7873229980469, 175.65663146972656], "page": 7, "span": [0, 50], "__ref_s3_data": null}], "text": "FT: Model was trained on PubTabNet then finetuned.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11201477050781, 78.84806823730469, 286.3659973144531, 147.6501922607422], "page": 7, "span": [0, 346], "__ref_s3_data": null}], "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1210021972656, 48.960079193115234, 300.102294921875, 57.866641998291016], "page": 7, "span": [0, 1], "__ref_s3_data": null}], "text": "7", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.86199951171875, 564.4229125976562, 545.1151733398438, 716.7916259765625], "page": 7, "span": [0, 737], "__ref_s3_data": null}], "text": "our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 454.68914794921875, 545.1151733398438, 475.5506896972656], "page": 7, "span": [0, 94], "__ref_s3_data": null}], "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/4"}, {"prov": [{"bbox": [308.8619689941406, 271.8323059082031, 545.1156616210938, 424.3202819824219], "page": 7, "span": [0, 715], "__ref_s3_data": null}], "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 102.32206726074219, 545.1151733398438, 135.13864135742188], "page": 7, "span": [0, 148], "__ref_s3_data": null}], "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/5"}, {"prov": [{"bbox": [53.28603744506836, 705.4392700195312, 61.550289154052734, 713.3124389648438], "page": 8, "span": [0, 2], "__ref_s3_data": null}], "text": "- a.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [65.68241882324219, 705.4392700195312, 499.5556335449219, 713.3124389648438], "page": 8, "span": [0, 105], "__ref_s3_data": null}], "text": "- Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.81178283691406, 689.845703125, 284.3459167480469, 697.7188720703125], "page": 8, "span": [0, 53], "__ref_s3_data": null}], "text": "Japanese language (previously unseen by TableFormer):", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [304.830810546875, 689.845703125, 431.0911865234375, 697.7188720703125], "page": 8, "span": [0, 29], "__ref_s3_data": null}], "text": "Example table from FinTabNet:", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/6"}, {"prov": [{"bbox": [53.81178283691406, 575.8935546875, 385.93450927734375, 583.7667236328125], "page": 8, "span": [0, 79], "__ref_s3_data": null}], "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/7"}, {"name": "Table", "type": "table", "$ref": "#/tables/6"}, {"prov": [{"bbox": [380.42730712890625, 493.39715576171875, 549.4217529296875, 499.69573974609375], "page": 8, "span": [0, 53], "__ref_s3_data": null}], "text": "Text is aligned to match original for ease of viewing", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/7"}, {"prov": [{"bbox": [50.11199951171875, 426.3501281738281, 545.11376953125, 471.1226501464844], "page": 8, "span": [0, 397], "__ref_s3_data": null}], "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/8"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/9"}, {"prov": [{"bbox": [62.595001220703125, 324.3650817871094, 532.6304931640625, 333.2716369628906], "page": 8, "span": [0, 112], "__ref_s3_data": null}], "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/10"}, {"prov": [{"bbox": [50.11199951171875, 290.7525939941406, 163.75579833984375, 300.6046447753906], "page": 8, "span": [0, 25], "__ref_s3_data": null}], "text": "5.5. Qualitative Analysis", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11199951171875, 78.84805297851562, 286.3651123046875, 255.1266326904297], "page": 8, "span": [0, 866], "__ref_s3_data": null}], "text": "We showcase several visualizations for the different components of our network on various \"complex\" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 290.5433654785156, 460.8484802246094, 301.29107666015625], "page": 8, "span": [0, 27], "__ref_s3_data": null}], "text": "6. Future Work & Conclusion", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.86199951171875, 138.69407653808594, 545.1151733398438, 279.10662841796875], "page": 8, "span": [0, 640], "__ref_s3_data": null}], "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \"SynthTabNet\" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 109.15335845947266, 364.4058532714844, 119.90107727050781], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "References", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [313.3450012207031, 79.06324768066406, 545.1134033203125, 98.0382080078125], "page": 8, "span": [0, 121], "__ref_s3_data": null}], "text": "- [1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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Proceedings of the AAAI Conference on Artificial Intelligence , 35(17):15137-15145, May 2021. 1", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 421.2228698730469, 545.1160888671875, 462.1158142089844], "page": 9, "span": [0, 229], "__ref_s3_data": null}], "text": "- [20] Rujiao Long, Wen Wang, Nan Xue, Feiyu Gao, Zhibo Yang, Yongpan Wang, and Gui-Song Xia. Parsing table structures in the wild. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 944-952, 2021. 2", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 354.9829406738281, 545.1134643554688, 417.7938232421875], "page": 9, "span": [0, 315], "__ref_s3_data": null}], "text": "- [21] Shubham Singh Paliwal, D Vishwanath, Rohit Rahul, Monika Sharma, and Lovekesh Vig. 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Garnett, editors, Advances in Neural Information Processing Systems 32 , pages 8024-8035. Curran Associates, Inc., 2019. 6", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 167.7090301513672, 545.1134033203125, 230.5189971923828], "page": 9, "span": [0, 322], "__ref_s3_data": null}], "text": "- [23] Devashish Prasad, Ayan Gadpal, Kshitij Kapadni, Manish Visave, and Kavita Sultanpure. Cascadetabnet: An approach for end to end table detection and structure recognition from image-based documents. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , pages 572-573, 2020. 1", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 123.38601684570312, 545.1162109375, 164.27899169921875], "page": 9, "span": [0, 224], "__ref_s3_data": null}], "text": "- [24] Shah Rukh Qasim, Hassan Mahmood, and Faisal Shafait. 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IEEE, 2019. 3", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.112022399902344, 200.70655822753906, 286.3633728027344, 241.59951782226562], "page": 10, "span": [0, 190], "__ref_s3_data": null}], "text": "- [34] Wenyuan Xue, Baosheng Yu, Wen Wang, Dacheng Tao, and Qingyong Li. Tgrnet: A table graph reconstruction network for table structure recognition. arXiv preprint arXiv:2106.10598 , 2021. 3", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.112030029296875, 156.50555419921875, 286.3634033203125, 197.3985137939453], "page": 10, "span": [0, 220], "__ref_s3_data": null}], "text": "- [35] Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo. Image captioning with semantic attention. 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Image-based table recognition: Data, model,", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [292.6300048828125, 48.960445404052734, 302.59259033203125, 57.867008209228516], "page": 10, "span": [0, 2], "__ref_s3_data": null}], "text": "10", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [328.781005859375, 675.2245483398438, 545.1145629882812, 716.1165161132812], "page": 10, "span": [0, 192], "__ref_s3_data": null}], "text": "- and evaluation. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Computer Vision ECCV 2020 , pages 564-580, Cham, 2020. Springer International Publishing. 2, 3, 7", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 630.392578125, 545.1133422851562, 671.2855224609375], "page": 10, "span": [0, 221], "__ref_s3_data": null}], "text": "- [38] Xu Zhong, Jianbin Tang, and Antonio Jimeno Yepes. Publaynet: Largest dataset ever for document layout analysis. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [132.8419952392578, 656.4699096679688, 465.37591552734375, 681.4251098632812], "page": 11, "span": [0, 83], "__ref_s3_data": null}], "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 620.0913696289062, 175.96437072753906, 630.839111328125], "page": 11, "span": [0, 26], "__ref_s3_data": null}], "text": "1. Details on the datasets", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 601.1686401367188, 150.364013671875, 611.0206909179688], "page": 11, "span": [0, 21], "__ref_s3_data": null}], "text": "1.1. Data preparation", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 403.8451843261719, 286.3651428222656, 592.0797119140625], "page": 11, "span": [0, 931], "__ref_s3_data": null}], "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 164.54029846191406, 286.3651123046875, 400.5947265625], "page": 11, "span": [0, 1149], "__ref_s3_data": null}], "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 140.42730712890625, 286.3649597167969, 161.28985595703125], "page": 11, "span": [0, 92], "__ref_s3_data": null}], "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 119.7578125, 153.60784912109375, 129.60986328125], "page": 11, "span": [0, 23], "__ref_s3_data": null}], "text": "1.2. Synthetic datasets", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 77.852294921875, 286.36505126953125, 110.66886901855469], "page": 11, "span": [0, 167], "__ref_s3_data": null}], "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 584.572265625, 545.1151123046875, 629.3448486328125], "page": 11, "span": [0, 221], "__ref_s3_data": null}], "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 559.9032592773438, 545.1150512695312, 580.7648315429688], "page": 11, "span": [0, 89], "__ref_s3_data": null}], "text": "The process of generating a synthetic dataset can be decomposed into the following steps:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 475.45721435546875, 545.1151123046875, 556.0947875976562], "page": 11, "span": [0, 373], "__ref_s3_data": null}], "text": "- 1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 343.19134521484375, 545.1151733398438, 471.6497802734375], "page": 11, "span": [0, 573], "__ref_s3_data": null}], "text": "- 2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 294.61138916015625, 545.1151733398438, 339.3839111328125], "page": 11, "span": [0, 195], "__ref_s3_data": null}], "text": "- 3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 246.0314178466797, 545.1152954101562, 290.803955078125], "page": 11, "span": [0, 218], "__ref_s3_data": null}], "text": "- 4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 185.4964141845703, 545.1151733398438, 242.22396850585938], "page": 11, "span": [0, 238], "__ref_s3_data": null}], "text": "- 5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 145.01368713378906, 545.1087646484375, 169.70941162109375], "page": 11, "span": [0, 47], "__ref_s3_data": null}], "text": "2. Prediction post-processing for PDF documents", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.8620300292969, 77.85139465332031, 545.1151733398438, 134.57896423339844], "page": 11, "span": [0, 247], "__ref_s3_data": null}], "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [292.63104248046875, 48.96039962768555, 302.5936279296875, 57.86696243286133], "page": 11, "span": [0, 2], "__ref_s3_data": null}], "text": "11", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [50.11199951171875, 605.6360473632812, 545.1137084960938, 626.4976196289062], "page": 12, "span": [0, 245], "__ref_s3_data": null}], "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/11"}, {"prov": [{"bbox": [61.569000244140625, 560.20703125, 286.3651123046875, 581.068603515625], "page": 12, "span": [0, 61], "__ref_s3_data": null}], "text": "- \u00b7 TableFormer output does not include the table cell content.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.569000244140625, 527.0670166015625, 286.3651428222656, 547.9285888671875], "page": 12, "span": [0, 77], "__ref_s3_data": null}], "text": "- \u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 396.2931213378906, 286.3651123046875, 512.7965698242188], "page": 12, "span": [0, 545], "__ref_s3_data": null}], "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 404.08929443359375, 545.1151123046875, 508.6367492675781], "page": 12, "span": [0, 471], "__ref_s3_data": null}], "text": "- 7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 372.068115234375, 286.3649597167969, 392.9306640625], "page": 12, "span": [0, 68], "__ref_s3_data": null}], "text": "Here is a step-by-step description of the prediction postprocessing:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 335.8881530761719, 286.3650817871094, 368.7046813964844], "page": 12, "span": [0, 173], "__ref_s3_data": null}], "text": "- 1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 287.7532043457031, 286.36505126953125, 332.52471923828125], "page": 12, "span": [0, 187], "__ref_s3_data": null}], "text": "- 2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 263.5272216796875, 286.36492919921875, 284.3897705078125], "page": 12, "span": [0, 97], "__ref_s3_data": null}], "text": "- 3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 227.34722900390625, 286.3651123046875, 260.164794921875], "page": 12, "span": [0, 131], "__ref_s3_data": null}], "text": "- 3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 191.16722106933594, 286.3650817871094, 223.98377990722656], "page": 12, "span": [0, 169], "__ref_s3_data": null}], "text": "- 4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.8620300292969, 187.8454132080078, 545.1168823242188, 220.66197204589844], "page": 12, "span": [0, 113], "__ref_s3_data": null}], "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [110.70498657226562, 137.89439392089844, 286.3623962402344, 168.5640869140625], "page": 12, "span": [0, 81], "__ref_s3_data": null}], "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } - min { x$_{c}$ } (4)", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.11199951171875, 103.07321166992188, 286.36199951171875, 124.6520767211914], "page": 12, "span": [0, 103], "__ref_s3_data": null}], "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862060546875, 103.10841369628906, 545.114990234375, 123.969970703125], "page": 12, "span": [0, 107], "__ref_s3_data": null}], "text": "- 9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 78.84821319580078, 286.3649597167969, 99.70977783203125], "page": 12, "span": [0, 110], "__ref_s3_data": null}], "text": "- 5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862060546875, 78.84840393066406, 545.1151733398438, 99.70997619628906], "page": 12, "span": [0, 118], "__ref_s3_data": null}], "text": "- 9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862060546875, 163.58441162109375, 545.1150512695312, 184.44696044921875], "page": 12, "span": [0, 101], "__ref_s3_data": null}], "text": "- 9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862060546875, 127.3694076538086, 545.1150512695312, 160.18597412109375], "page": 12, "span": [0, 117], "__ref_s3_data": null}], "text": "- 9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.8620300292969, 332.00836181640625, 545.1151733398438, 400.6898498535156], "page": 12, "span": [0, 311], "__ref_s3_data": null}], "text": "- 8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.8620300292969, 224.06141662597656, 545.1151733398438, 328.6089172363281], "page": 12, "span": [0, 503], "__ref_s3_data": null}], "text": "- 9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 536.2962036132812, 545.1151733398438, 581.0687866210938], "page": 12, "span": [0, 183], "__ref_s3_data": null}], "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 512.0361938476562, 545.114990234375, 532.8977661132812], "page": 12, "span": [0, 91], "__ref_s3_data": null}], "text": "- 6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [292.6310729980469, 48.96040725708008, 302.5936584472656, 57.86697006225586], "page": 12, "span": [0, 2], "__ref_s3_data": null}], "text": "12", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [50.11199951171875, 707.8850708007812, 88.84658813476562, 716.7916259765625], "page": 13, "span": [0, 10], "__ref_s3_data": null}], "text": "phan cell.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 683.9750366210938, 286.3649597167969, 704.8366088867188], "page": 13, "span": [0, 76], "__ref_s3_data": null}], "text": "9f. Otherwise create a new structural cell and match it wit the orphan cell.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 660.2941284179688, 286.364990234375, 680.8369140625], "page": 13, "span": [0, 97], "__ref_s3_data": null}], "text": "Aditional images with examples of TableFormer predictions and post-processing can be found below.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/8"}, {"name": "Table", "type": "table", "$ref": "#/tables/9"}, {"name": "Table", "type": "table", "$ref": "#/tables/10"}, {"prov": [{"bbox": [63.340999603271484, 281.0370788574219, 273.1334228515625, 289.9436340332031], "page": 13, "span": [0, 52], "__ref_s3_data": null}], "text": "Figure 8: Example of a table with multi-line header.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/11"}, {"prov": [{"bbox": [292.6309814453125, 48.960079193115234, 302.59356689453125, 57.866641998291016], "page": 13, "span": [0, 2], "__ref_s3_data": null}], "text": "13", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/12"}, {"name": "Table", "type": "table", "$ref": "#/tables/13"}, {"name": "Table", "type": "table", "$ref": "#/tables/14"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/12"}, {"prov": [{"bbox": [308.86199951171875, 464.54010009765625, 545.1151123046875, 485.4016418457031], "page": 13, "span": [0, 67], "__ref_s3_data": null}], "text": "Figure 9: Example of a table with big empty distance between cells.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/15"}, {"name": "Table", "type": "table", "$ref": "#/tables/16"}, {"name": "Table", "type": "table", "$ref": "#/tables/17"}, {"name": "Table", "type": "table", "$ref": "#/tables/18"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/13"}, {"prov": [{"bbox": [312.3429870605469, 102.60006713867188, 541.63232421875, 111.50663757324219], "page": 13, "span": [0, 55], "__ref_s3_data": null}], "text": "Figure 10: Example of a complex table with empty cells.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/19"}, {"name": "Table", "type": "table", "$ref": "#/tables/20"}, {"prov": [{"bbox": [50.11199951171875, 414.36810302734375, 286.3650817871094, 435.2296447753906], "page": 14, "span": [0, 61], "__ref_s3_data": null}], "text": "Figure 11: Simple table with different style and empty cells.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/14"}, {"name": "Table", "type": "table", "$ref": "#/tables/21"}, {"name": "Table", "type": "table", "$ref": "#/tables/22"}, {"name": "Table", "type": "table", "$ref": "#/tables/23"}, {"prov": [{"bbox": [54.61899948120117, 111.27507781982422, 281.85589599609375, 120.181640625], "page": 14, "span": [0, 56], "__ref_s3_data": null}], "text": "Figure 12: Simple table predictions and post processing.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/15"}, {"prov": [{"bbox": [292.6309814453125, 48.96007537841797, 302.59356689453125, 57.86663818359375], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "14", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/24"}, {"name": "Table", "type": "table", "$ref": "#/tables/25"}, {"name": "Table", "type": "table", "$ref": "#/tables/26"}, {"prov": [{"bbox": [315.7900085449219, 411.4090881347656, 538.1852416992188, 420.3156433105469], "page": 14, "span": [0, 55], "__ref_s3_data": null}], "text": "Figure 13: Table predictions example on colorful table.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/16"}, {"name": "Table", "type": "table", "$ref": "#/tables/27"}, {"name": "Table", "type": "table", "$ref": "#/tables/28"}, {"name": "Table", "type": "table", "$ref": "#/tables/29"}, {"prov": [{"bbox": [344.9849853515625, 99.54707336425781, 508.9893493652344, 108.45364379882812], "page": 14, "span": [0, 40], "__ref_s3_data": null}], "text": "Figure 14: Example with multi-line text.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/30"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/17"}, {"name": "Table", "type": "table", "$ref": "#/tables/31"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/18"}, {"name": "Table", "type": "table", "$ref": "#/tables/32"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/19"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/20"}, {"prov": [{"bbox": [84.23300170898438, 138.7420654296875, 252.24224853515625, 147.64862060546875], "page": 15, "span": [0, 41], "__ref_s3_data": null}], "text": "Figure 15: Example with triangular table.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/33"}, {"prov": [{"bbox": [292.6309814453125, 48.9600944519043, 302.59356689453125, 57.86665725708008], "page": 15, "span": [0, 2], "__ref_s3_data": null}], "text": "15", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/34"}, {"name": "Table", "type": "table", "$ref": "#/tables/35"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/21"}, {"name": "Table", "type": "table", "$ref": "#/tables/36"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/22"}, {"prov": [{"bbox": [308.8619689941406, 118.20308685302734, 545.1151123046875, 139.0646514892578], "page": 15, "span": [0, 106], "__ref_s3_data": null}], "text": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/37"}, {"prov": [{"bbox": [50.11199951171875, 262.80108642578125, 545.1138305664062, 283.6626281738281], "page": 16, "span": [0, 153], "__ref_s3_data": null}], "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/23"}, {"prov": [{"bbox": [292.6309814453125, 48.960079193115234, 302.59356689453125, 57.866641998291016], "page": 16, "span": [0, 2], "__ref_s3_data": null}], "text": "16", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}], "figures": [{"prov": [{"bbox": [315.65362548828125, 489.19854736328125, 537.1475219726562, 563.2765502929688], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [314.78173828125, 381.9505615234375, 539.1802978515625, 453.9347229003906], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [315.7172546386719, 295.9709777832031, 536.835693359375, 358.176513671875], "page": 1, "span": [0, 220], "__ref_s3_data": null}], "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [312.10369873046875, 541.39013671875, 550.38916015625, 713.5591430664062], "page": 3, "span": [0, 104], "__ref_s3_data": null}], "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [74.30538940429688, 608.2989501953125, 519.9801025390625, 714.0888061523438], "page": 5, "span": [0, 212], "__ref_s3_data": null}], "text": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [53.033172607421875, 284.3311767578125, 285.3731384277344, 534.3345947265625], "page": 5, "span": [0, 745], "__ref_s3_data": null}], "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [49.97501754760742, 604.4212646484375, 301.6349182128906, 688.2876586914062], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [305.5844421386719, 611.374267578125, 554.8255615234375, 693.3489990234375], "page": 8, "span": [0, 79], "__ref_s3_data": null}], "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [51.73618698120117, 348.34197998046875, 211.83766174316406, 411.51922607421875], "page": 8, "span": [0, 397], "__ref_s3_data": null}], "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [216.76930236816406, 348.65301513671875, 375.7828674316406, 411.50933837890625], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [383.1363220214844, 349.2249755859375, 542.1131591796875, 410.7686767578125], "page": 8, "span": [0, 112], "__ref_s3_data": null}], "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [53.54228973388672, 644.4091186523438, 544.938232421875, 717.25146484375], "page": 12, "span": [0, 245], "__ref_s3_data": null}], "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [309.79150390625, 499.60601806640625, 425.9603271484375, 538.0946044921875], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [333.9573669433594, 126.5096435546875, 518.4768676757812, 198.8865966796875], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [51.1537971496582, 447.0933532714844, 282.8598937988281, 687.6914672851562], "page": 14, "span": [0, 61], "__ref_s3_data": null}], "text": "Figure 11: Simple table with different style and empty cells.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [50.404788970947266, 135.83905029296875, 177.05642700195312, 180.99615478515625], "page": 14, "span": [0, 56], "__ref_s3_data": null}], "text": "Figure 12: Simple table predictions and post processing.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [318.6332092285156, 432.9424133300781, 534.73583984375, 701.1157836914062], "page": 14, "span": [0, 55], "__ref_s3_data": null}], "text": "Figure 13: Table predictions example on colorful table.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [55.1163444519043, 542.66552734375, 279.370849609375, 655.7449951171875], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [54.28135299682617, 418.4728698730469, 279.2568359375, 531.7384033203125], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [55.423954010009766, 294.436279296875, 280.2310791015625, 407.4449462890625], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [50.64816665649414, 160.73651123046875, 319.91033935546875, 286.0196838378906], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [323.4686279296875, 327.739501953125, 525.9568481445312, 429.5491638183594], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [353.6920471191406, 156.22674560546875, 495.4288024902344, 304.594970703125], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [66.79946899414062, 293.8616027832031, 528.5564575195312, 538.3836669921875], "page": 16, "span": [0, 153], "__ref_s3_data": null}], "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [331.1968078613281, 512.5169067382812, 457.95050048828125, 556.6529541015625], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "type": "table", "payload": null, "#-cols": 2, "#-rows": 2, "data": [[{"bbox": null, "spans": [[0, 0]], "text": "", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [384.03289794921875, 529.1906127929688, 390.0376892089844, 539.321044921875], "spans": [[0, 1]], "text": "3", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [331.1968078613281, 512.5169067382812, 337.20159912109375, 522.6473388671875], "spans": [[1, 0]], "text": "2", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": null, "spans": [[1, 1]], "text": "", "type": "body", "col": 1, "col-header": false, 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{"prov": [{"bbox": [317.05999755859375, 637.3401489257812, 536.9143676757812, 718.3856201171875], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. 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"text": "3", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [476.4010009765625, 637.35009765625, 496.3262023925781, 646.2566528320312], "spans": [[6, 3]], "text": "600k", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [512.6349487304688, 637.35009765625, 532.5601196289062, 646.2566528320312], "spans": [[6, 4]], "text": "PNG", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 6, "row-header": false, "row-span": [6, 7]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [66.31500244140625, 215.09107971191406, 282.1144104003906, 377.2876281738281], "page": 7, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).", "type": "table", "payload": null, "#-cols": 5, "#-rows": 11, "data": [[{"bbox": 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Picture of a table:", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.111976623535156, 241.30950927734375, 126.94803619384766, 252.05723571777344], "page": 1, "span": [0, 15], "__ref_s3_data": null}], "text": "1. Introduction", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.111976623535156, 78.84822082519531, 286.3650817871094, 231.216796875], "page": 1, "span": [0, 712], "__ref_s3_data": null}], "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [50.111976623535156, 279.00335693359375, 286.3651123046875, 550.6049194335938], "page": 1, "span": [0, 1320], "__ref_s3_data": null}], "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [315.5670166015625, 458.7572021484375, 486.4019470214844, 478.3052062988281], "page": 1, "span": [0, 68], "__ref_s3_data": null}], "text": "- b. Red-annotation of bounding boxes, Blue-predictions by TableFormer", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"prov": [{"bbox": [315.5670166015625, 363.0691833496094, 491.1912536621094, 371.81719970703125], "page": 1, "span": [0, 38], "__ref_s3_data": null}], "text": "- c. Structure predicted by TableFormer:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"prov": [{"bbox": [308.86199951171875, 232.7270965576172, 545.1151733398438, 277.4996337890625], "page": 1, "span": [0, 220], "__ref_s3_data": null}], "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [308.86199951171875, 126.95307159423828, 545.1151733398438, 207.59063720703125], "page": 1, "span": [0, 363], "__ref_s3_data": null}], "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 78.84806823730469, 545.1151123046875, 123.61963653564453], "page": 1, "span": [0, 229], "__ref_s3_data": null}], "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1210021972656, 48.9600715637207, 300.102294921875, 57.866634368896484], "page": 1, "span": [0, 1], "__ref_s3_data": null}], "text": "1", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [50.11199951171875, 695.9300537109375, 286.36505126953125, 716.7916259765625], "page": 2, "span": [0, 75], "__ref_s3_data": null}], "text": "considered as a solved problem, given enough ground-truth data to train on.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 563.9699096679688, 286.3651428222656, 692.4285888671875], "page": 2, "span": [0, 626], "__ref_s3_data": null}], "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 420.054931640625, 286.3651123046875, 560.4684448242188], "page": 2, "span": [0, 643], "__ref_s3_data": null}], "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 359.8269958496094, 286.3665771484375, 416.5534973144531], "page": 2, "span": [0, 242], "__ref_s3_data": null}], "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [61.56901550292969, 302.6770324707031, 286.3648986816406, 347.568115234375], "page": 2, "span": [0, 166], "__ref_s3_data": null}], "text": "- \u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.56901550292969, 245.0740509033203, 286.3648986816406, 289.9661560058594], "page": 2, "span": [0, 181], "__ref_s3_data": null}], "text": "- \u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.569000244140625, 199.4270477294922, 286.36492919921875, 232.3631591796875], "page": 2, "span": [0, 106], "__ref_s3_data": null}], "text": "- \u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.569007873535156, 153.779052734375, 286.3650817871094, 186.5966033935547], "page": 2, "span": [0, 131], "__ref_s3_data": null}], "text": "- \u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11200714111328, 96.63004302978516, 286.3651123046875, 141.401611328125], "page": 2, "span": [0, 231], "__ref_s3_data": null}], "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [60.97100067138672, 79.27845764160156, 183.7305450439453, 86.40372467041016], "page": 2, "span": [0, 40], "__ref_s3_data": null}], "text": "$^{1}$https://github.com/IBM/SynthTabNet", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [295.1210021972656, 48.96015548706055, 300.102294921875, 57.86671829223633], "page": 2, "span": [0, 1], "__ref_s3_data": null}], "text": "2", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.86199951171875, 683.9750366210938, 545.1151123046875, 716.7916259765625], "page": 2, "span": [0, 166], "__ref_s3_data": null}], "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 659.5203247070312, 498.28021240234375, 670.26806640625], "page": 2, "span": [0, 37], "__ref_s3_data": null}], "text": "2. Previous work and State of the Art", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.86199951171875, 461.54498291015625, 545.1151733398438, 649.7786254882812], "page": 2, "span": [0, 901], "__ref_s3_data": null}], "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 341.9270935058594, 545.115234375, 458.4305419921875], "page": 2, "span": [0, 552], "__ref_s3_data": null}], "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619689941406, 78.84815216064453, 545.1168823242188, 338.9322204589844], "page": 2, "span": [0, 1262], "__ref_s3_data": null}], "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \"image-encoder \u2192 text-decoder\" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \"image-encoder \u2192 dual decoder\" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 707.8850708007812, 250.15101623535156, 716.7916259765625], "page": 3, "span": [0, 51], "__ref_s3_data": null}], "text": "tag-decoder which is constrained to the table-tags.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 516.5458984375, 286.3651428222656, 704.7806396484375], "page": 3, "span": [0, 864], "__ref_s3_data": null}], "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199188232422, 301.297119140625, 286.3651123046875, 513.56103515625], "page": 3, "span": [0, 1007], "__ref_s3_data": null}], "text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 169.733154296875, 286.36627197265625, 298.3112487792969], "page": 3, "span": [0, 619], "__ref_s3_data": null}], "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 145.30743408203125, 105.22545623779297, 156.05516052246094], "page": 3, "span": [0, 11], "__ref_s3_data": null}], "text": "3. Datasets", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 78.84813690185547, 286.3650817871094, 135.57470703125], "page": 3, "span": [0, 281], "__ref_s3_data": null}], "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1210021972656, 48.96023941040039, 300.102294921875, 57.86680221557617], "page": 3, "span": [0, 1], "__ref_s3_data": null}], "text": "3", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.86199951171875, 503.3020935058594, 545.1151123046875, 524.1636352539062], "page": 3, "span": [0, 104], "__ref_s3_data": null}], "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [308.86199951171875, 465.6200866699219, 437.27001953125, 474.5266418457031], "page": 3, "span": [0, 33], "__ref_s3_data": null}], "text": "balance in the previous datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 164.6382598876953, 545.1151733398438, 460.4686279296875], "page": 3, "span": [0, 1400], "__ref_s3_data": null}], "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \"simple\" when it does not contain row spans or column spans, otherwise it is \"complex\". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 78.84823608398438, 545.1151123046875, 159.48580932617188], "page": 3, "span": [0, 406], "__ref_s3_data": null}], "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 695.9300537109375, 286.3651123046875, 716.7916259765625], "page": 4, "span": [0, 93], "__ref_s3_data": null}], "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 478.8949279785156, 286.3651428222656, 691.0396118164062], "page": 4, "span": [0, 983], "__ref_s3_data": null}], "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 357.50103759765625, 286.3651123046875, 474.0044860839844], "page": 4, "span": [0, 571], "__ref_s3_data": null}], "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 164.37611389160156, 286.3665466308594, 352.610595703125], "page": 4, "span": [0, 941], "__ref_s3_data": null}], "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11201477050781, 78.84810638427734, 286.3651123046875, 159.4856719970703], "page": 4, "span": [0, 405], "__ref_s3_data": null}], "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1209716796875, 48.96018600463867, 300.1022644042969, 57.86674880981445], "page": 4, "span": [0, 1], "__ref_s3_data": null}], "text": "4", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.86199951171875, 567.6110229492188, 545.1150512695312, 624.338623046875], "page": 4, "span": [0, 267], "__ref_s3_data": null}], "text": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"prov": [{"bbox": [308.86199951171875, 497.6080322265625, 545.1151733398438, 542.3795776367188], "page": 4, "span": [0, 210], "__ref_s3_data": null}], "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.8169860839844, 485.321044921875, 542.7439575195312, 494.22760009765625], "page": 4, "span": [0, 57], "__ref_s3_data": null}], "text": "Tab. 1 summarizes the various attributes of the datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 460.0683288574219, 444.9360656738281, 470.8160400390625], "page": 4, "span": [0, 24], "__ref_s3_data": null}], "text": "4. The TableFormer model", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.86199951171875, 345.5131530761719, 545.115234375, 450.06060791015625], "page": 4, "span": [0, 504], "__ref_s3_data": null}], "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 324.45367431640625, 420.16058349609375, 334.30572509765625], "page": 4, "span": [0, 24], "__ref_s3_data": null}], "text": "4.1. Model architecture.", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.8619689941406, 127.00019073486328, 545.11572265625, 315.2347106933594], "page": 4, "span": [0, 907], "__ref_s3_data": null}], "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619689941406, 78.84818267822266, 545.1151123046875, 123.73930358886719], "page": 4, "span": [0, 223], "__ref_s3_data": null}], "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199188232422, 567.0330810546875, 545.1084594726562, 588.0142211914062], "page": 5, "span": [0, 212], "__ref_s3_data": null}], "text": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [50.11199951171875, 111.72905731201172, 286.365966796875, 264.2171936035156], "page": 5, "span": [0, 745], "__ref_s3_data": null}], "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [308.86199951171875, 497.69305419921875, 545.1150512695312, 542.465576171875], "page": 5, "span": [0, 227], "__ref_s3_data": null}], "text": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619384765625, 378.0381774902344, 545.1151123046875, 494.6601867675781], "page": 5, "span": [0, 563], "__ref_s3_data": null}], "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \"Scene Understanding\", \"Image Captioning\"), something which we relate to the simplicity of table images.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619689941406, 246.4272918701172, 545.1151123046875, 374.8857421875], "page": 5, "span": [0, 592], "__ref_s3_data": null}], "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619384765625, 138.727294921875, 545.1151123046875, 243.39540100097656], "page": 5, "span": [0, 483], "__ref_s3_data": null}], "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619384765625, 78.84827423095703, 545.1150512695312, 135.57484436035156], "page": 5, "span": [0, 286], "__ref_s3_data": null}], "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1209411621094, 48.96027755737305, 300.10223388671875, 57.86684036254883], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "5", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [50.11199951171875, 636.1539916992188, 286.3651428222656, 716.7916259765625], "page": 6, "span": [0, 380], "__ref_s3_data": null}], "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 551.7369384765625, 286.3651123046875, 632.3755493164062], "page": 6, "span": [0, 371], "__ref_s3_data": null}], "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 347.76910400390625, 286.36572265625, 548.0780639648438], "page": 6, "span": [0, 985], "__ref_s3_data": null}], "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112022399902344, 323.12811279296875, 286.364990234375, 343.9896545410156], "page": 6, "span": [0, 67], "__ref_s3_data": null}], "text": "The loss used to train the TableFormer can be defined as following:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [124.33001708984375, 274.92828369140625, 286.3624267578125, 298.71905517578125], "page": 6, "span": [0, 84], "__ref_s3_data": null}], "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 - \u03bb ) l$_{box}$ (1)", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.112030029296875, 251.78411865234375, 281.596923828125, 261.4079895019531], "page": 6, "span": [0, 76], "__ref_s3_data": null}], "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11204528808594, 225.33538818359375, 171.9833526611328, 236.08311462402344], "page": 6, "span": [0, 23], "__ref_s3_data": null}], "text": "5. Experimental Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11204528808594, 205.8836212158203, 179.17501831054688, 215.7356719970703], "page": 6, "span": [0, 27], "__ref_s3_data": null}], "text": "5.1. Implementation Details", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11204528808594, 151.4931182861328, 286.36517333984375, 196.2656707763672], "page": 6, "span": [0, 207], "__ref_s3_data": null}], "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [91.66104888916016, 113.60411834716797, 286.3624572753906, 138.1719970703125], "page": 6, "span": [0, 77], "__ref_s3_data": null}], "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.112060546875, 78.8481216430664, 286.3651428222656, 99.70968627929688], "page": 6, "span": [0, 117], "__ref_s3_data": null}], "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.12103271484375, 48.96010971069336, 300.1023254394531, 57.86667251586914], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "6", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.862060546875, 683.97509765625, 545.115234375, 716.7916870117188], "page": 6, "span": [0, 156], "__ref_s3_data": null}], "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862060546875, 463.6259460449219, 545.1152954101562, 675.7706298828125], "page": 6, "span": [0, 1024], "__ref_s3_data": null}], "text": "The Transformer Encoder consists of two \"Transformer Encoder Layers\", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \"Transformer Decoder Layers\" with similar input and output dimensions as the \"Transformer Encoder Layers\". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 362.83001708984375, 545.1151733398438, 455.4224853515625], "page": 6, "span": [0, 419], "__ref_s3_data": null}], "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 238.12310791015625, 545.115234375, 354.6255798339844], "page": 6, "span": [0, 528], "__ref_s3_data": null}], "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 202.5936279296875, 397.44281005859375, 212.4456787109375], "page": 6, "span": [0, 19], "__ref_s3_data": null}], "text": "5.2. Generalization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.8620300292969, 119.86811065673828, 545.1151733398438, 188.55067443847656], "page": 6, "span": [0, 299], "__ref_s3_data": null}], "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 78.84710693359375, 545.115234375, 111.6646728515625], "page": 6, "span": [0, 155], "__ref_s3_data": null}], "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 707.74658203125, 167.89825439453125, 717.5986328125], "page": 7, "span": [0, 25], "__ref_s3_data": null}], "text": "5.3. Datasets and Metrics", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11199951171875, 653.8770141601562, 286.3651123046875, 698.6495971679688], "page": 7, "span": [0, 192], "__ref_s3_data": null}], "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [86.218994140625, 619.26123046875, 286.3623962402344, 641.6820068359375], "page": 7, "span": [0, 99], "__ref_s3_data": null}], "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 - EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.11198425292969, 578.02099609375, 286.36285400390625, 610.9970092773438], "page": 7, "span": [0, 162], "__ref_s3_data": null}], "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 557.3284912109375, 170.45169067382812, 567.1805419921875], "page": 7, "span": [0, 26], "__ref_s3_data": null}], "text": "5.4. Quantitative Analysis", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11199951171875, 395.862060546875, 286.3651428222656, 548.35009765625], "page": 7, "span": [0, 723], "__ref_s3_data": null}], "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 178.705078125, 286.3651123046875, 199.56663513183594], "page": 7, "span": [0, 101], "__ref_s3_data": null}], "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/3"}, {"prov": [{"bbox": [50.11199951171875, 166.7500762939453, 261.7873229980469, 175.65663146972656], "page": 7, "span": [0, 50], "__ref_s3_data": null}], "text": "FT: Model was trained on PubTabNet then finetuned.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11201477050781, 78.84806823730469, 286.3659973144531, 147.6501922607422], "page": 7, "span": [0, 346], "__ref_s3_data": null}], "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1210021972656, 48.960079193115234, 300.102294921875, 57.866641998291016], "page": 7, "span": [0, 1], "__ref_s3_data": null}], "text": "7", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.86199951171875, 564.4229125976562, 545.1151733398438, 716.7916259765625], "page": 7, "span": [0, 737], "__ref_s3_data": null}], "text": "our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 454.68914794921875, 545.1151733398438, 475.5506896972656], "page": 7, "span": [0, 94], "__ref_s3_data": null}], "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/4"}, {"prov": [{"bbox": [308.8619689941406, 271.8323059082031, 545.1156616210938, 424.3202819824219], "page": 7, "span": [0, 715], "__ref_s3_data": null}], "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 102.32206726074219, 545.1151733398438, 135.13864135742188], "page": 7, "span": [0, 148], "__ref_s3_data": null}], "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/5"}, {"prov": [{"bbox": [53.28603744506836, 705.4392700195312, 61.550289154052734, 713.3124389648438], "page": 8, "span": [0, 2], "__ref_s3_data": null}], "text": "- a.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [65.68241882324219, 705.4392700195312, 499.5556335449219, 713.3124389648438], "page": 8, "span": [0, 105], "__ref_s3_data": null}], "text": "- Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.81178283691406, 689.845703125, 284.3459167480469, 697.7188720703125], "page": 8, "span": [0, 53], "__ref_s3_data": null}], "text": "Japanese language (previously unseen by TableFormer):", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [304.830810546875, 689.845703125, 431.0911865234375, 697.7188720703125], "page": 8, "span": [0, 29], "__ref_s3_data": null}], "text": "Example table from FinTabNet:", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/6"}, {"prov": [{"bbox": [53.81178283691406, 575.8935546875, 385.93450927734375, 583.7667236328125], "page": 8, "span": [0, 79], "__ref_s3_data": null}], "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/7"}, {"name": "Table", "type": "table", "$ref": "#/tables/6"}, {"prov": [{"bbox": [380.42730712890625, 493.39715576171875, 549.4217529296875, 499.69573974609375], "page": 8, "span": [0, 53], "__ref_s3_data": null}], "text": "Text is aligned to match original for ease of viewing", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/7"}, {"prov": [{"bbox": [50.11199951171875, 426.3501281738281, 545.11376953125, 471.1226501464844], "page": 8, "span": [0, 397], "__ref_s3_data": null}], "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/8"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/9"}, {"prov": [{"bbox": [62.595001220703125, 324.3650817871094, 532.6304931640625, 333.2716369628906], "page": 8, "span": [0, 112], "__ref_s3_data": null}], "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/10"}, {"prov": [{"bbox": [50.11199951171875, 290.7525939941406, 163.75579833984375, 300.6046447753906], "page": 8, "span": [0, 25], "__ref_s3_data": null}], "text": "5.5. Qualitative Analysis", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11199951171875, 78.84805297851562, 286.3651123046875, 255.1266326904297], "page": 8, "span": [0, 866], "__ref_s3_data": null}], "text": "We showcase several visualizations for the different components of our network on various \"complex\" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 290.5433654785156, 460.8484802246094, 301.29107666015625], "page": 8, "span": [0, 27], "__ref_s3_data": null}], "text": "6. Future Work & Conclusion", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.86199951171875, 138.69407653808594, 545.1151733398438, 279.10662841796875], "page": 8, "span": [0, 640], "__ref_s3_data": null}], "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \"SynthTabNet\" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 109.15335845947266, 364.4058532714844, 119.90107727050781], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "References", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [313.3450012207031, 79.06324768066406, 545.1134033203125, 98.0382080078125], "page": 8, "span": [0, 121], "__ref_s3_data": null}], "text": "- [1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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Proceedings of the AAAI Conference on Artificial Intelligence , 35(17):15137-15145, May 2021. 1", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 421.2228698730469, 545.1160888671875, 462.1158142089844], "page": 9, "span": [0, 229], "__ref_s3_data": null}], "text": "- [20] Rujiao Long, Wen Wang, Nan Xue, Feiyu Gao, Zhibo Yang, Yongpan Wang, and Gui-Song Xia. Parsing table structures in the wild. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 944-952, 2021. 2", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 354.9829406738281, 545.1134643554688, 417.7938232421875], "page": 9, "span": [0, 315], "__ref_s3_data": null}], "text": "- [21] Shubham Singh Paliwal, D Vishwanath, Rohit Rahul, Monika Sharma, and Lovekesh Vig. 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Garnett, editors, Advances in Neural Information Processing Systems 32 , pages 8024-8035. Curran Associates, Inc., 2019. 6", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 167.7090301513672, 545.1134033203125, 230.5189971923828], "page": 9, "span": [0, 322], "__ref_s3_data": null}], "text": "- [23] Devashish Prasad, Ayan Gadpal, Kshitij Kapadni, Manish Visave, and Kavita Sultanpure. Cascadetabnet: An approach for end to end table detection and structure recognition from image-based documents. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , pages 572-573, 2020. 1", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 123.38601684570312, 545.1162109375, 164.27899169921875], "page": 9, "span": [0, 224], "__ref_s3_data": null}], "text": "- [24] Shah Rukh Qasim, Hassan Mahmood, and Faisal Shafait. 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IEEE, 2019. 3", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.112022399902344, 200.70655822753906, 286.3633728027344, 241.59951782226562], "page": 10, "span": [0, 190], "__ref_s3_data": null}], "text": "- [34] Wenyuan Xue, Baosheng Yu, Wen Wang, Dacheng Tao, and Qingyong Li. Tgrnet: A table graph reconstruction network for table structure recognition. arXiv preprint arXiv:2106.10598 , 2021. 3", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.112030029296875, 156.50555419921875, 286.3634033203125, 197.3985137939453], "page": 10, "span": [0, 220], "__ref_s3_data": null}], "text": "- [35] Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo. Image captioning with semantic attention. 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Image-based table recognition: Data, model,", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [292.6300048828125, 48.960445404052734, 302.59259033203125, 57.867008209228516], "page": 10, "span": [0, 2], "__ref_s3_data": null}], "text": "10", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [328.781005859375, 675.2245483398438, 545.1145629882812, 716.1165161132812], "page": 10, "span": [0, 192], "__ref_s3_data": null}], "text": "- and evaluation. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Computer Vision ECCV 2020 , pages 564-580, Cham, 2020. Springer International Publishing. 2, 3, 7", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 630.392578125, 545.1133422851562, 671.2855224609375], "page": 10, "span": [0, 221], "__ref_s3_data": null}], "text": "- [38] Xu Zhong, Jianbin Tang, and Antonio Jimeno Yepes. Publaynet: Largest dataset ever for document layout analysis. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [132.8419952392578, 656.4699096679688, 465.37591552734375, 681.4251098632812], "page": 11, "span": [0, 83], "__ref_s3_data": null}], "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 620.0913696289062, 175.96437072753906, 630.839111328125], "page": 11, "span": [0, 26], "__ref_s3_data": null}], "text": "1. Details on the datasets", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 601.1686401367188, 150.364013671875, 611.0206909179688], "page": 11, "span": [0, 21], "__ref_s3_data": null}], "text": "1.1. Data preparation", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 403.8451843261719, 286.3651428222656, 592.0797119140625], "page": 11, "span": [0, 931], "__ref_s3_data": null}], "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 164.54029846191406, 286.3651123046875, 400.5947265625], "page": 11, "span": [0, 1149], "__ref_s3_data": null}], "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 140.42730712890625, 286.3649597167969, 161.28985595703125], "page": 11, "span": [0, 92], "__ref_s3_data": null}], "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 119.7578125, 153.60784912109375, 129.60986328125], "page": 11, "span": [0, 23], "__ref_s3_data": null}], "text": "1.2. Synthetic datasets", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 77.852294921875, 286.36505126953125, 110.66886901855469], "page": 11, "span": [0, 167], "__ref_s3_data": null}], "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 584.572265625, 545.1151123046875, 629.3448486328125], "page": 11, "span": [0, 221], "__ref_s3_data": null}], "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 559.9032592773438, 545.1150512695312, 580.7648315429688], "page": 11, "span": [0, 89], "__ref_s3_data": null}], "text": "The process of generating a synthetic dataset can be decomposed into the following steps:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 475.45721435546875, 545.1151123046875, 556.0947875976562], "page": 11, "span": [0, 373], "__ref_s3_data": null}], "text": "- 1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 343.19134521484375, 545.1151733398438, 471.6497802734375], "page": 11, "span": [0, 573], "__ref_s3_data": null}], "text": "- 2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 294.61138916015625, 545.1151733398438, 339.3839111328125], "page": 11, "span": [0, 195], "__ref_s3_data": null}], "text": "- 3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 246.0314178466797, 545.1152954101562, 290.803955078125], "page": 11, "span": [0, 218], "__ref_s3_data": null}], "text": "- 4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 185.4964141845703, 545.1151733398438, 242.22396850585938], "page": 11, "span": [0, 238], "__ref_s3_data": null}], "text": "- 5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 145.01368713378906, 545.1087646484375, 169.70941162109375], "page": 11, "span": [0, 47], "__ref_s3_data": null}], "text": "2. Prediction post-processing for PDF documents", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.8620300292969, 77.85139465332031, 545.1151733398438, 134.57896423339844], "page": 11, "span": [0, 247], "__ref_s3_data": null}], "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [292.63104248046875, 48.96039962768555, 302.5936279296875, 57.86696243286133], "page": 11, "span": [0, 2], "__ref_s3_data": null}], "text": "11", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [50.11199951171875, 605.6360473632812, 545.1137084960938, 626.4976196289062], "page": 12, "span": [0, 245], "__ref_s3_data": null}], "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/11"}, {"prov": [{"bbox": [61.569000244140625, 560.20703125, 286.3651123046875, 581.068603515625], "page": 12, "span": [0, 61], "__ref_s3_data": null}], "text": "- \u00b7 TableFormer output does not include the table cell content.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.569000244140625, 527.0670166015625, 286.3651428222656, 547.9285888671875], "page": 12, "span": [0, 77], "__ref_s3_data": null}], "text": "- \u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 396.2931213378906, 286.3651123046875, 512.7965698242188], "page": 12, "span": [0, 545], "__ref_s3_data": null}], "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 404.08929443359375, 545.1151123046875, 508.6367492675781], "page": 12, "span": [0, 471], "__ref_s3_data": null}], "text": "- 7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 372.068115234375, 286.3649597167969, 392.9306640625], "page": 12, "span": [0, 68], "__ref_s3_data": null}], "text": "Here is a step-by-step description of the prediction postprocessing:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 335.8881530761719, 286.3650817871094, 368.7046813964844], "page": 12, "span": [0, 173], "__ref_s3_data": null}], "text": "- 1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 287.7532043457031, 286.36505126953125, 332.52471923828125], "page": 12, "span": [0, 187], "__ref_s3_data": null}], "text": "- 2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 263.5272216796875, 286.36492919921875, 284.3897705078125], "page": 12, "span": [0, 97], "__ref_s3_data": null}], "text": "- 3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 227.34722900390625, 286.3651123046875, 260.164794921875], "page": 12, "span": [0, 131], "__ref_s3_data": null}], "text": "- 3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 191.16722106933594, 286.3650817871094, 223.98377990722656], "page": 12, "span": [0, 169], "__ref_s3_data": null}], "text": "- 4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.8620300292969, 187.8454132080078, 545.1168823242188, 220.66197204589844], "page": 12, "span": [0, 113], "__ref_s3_data": null}], "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [110.70498657226562, 137.89439392089844, 286.3623962402344, 168.5640869140625], "page": 12, "span": [0, 81], "__ref_s3_data": null}], "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } - min { x$_{c}$ } (4)", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.11199951171875, 103.07321166992188, 286.36199951171875, 124.6520767211914], "page": 12, "span": [0, 103], "__ref_s3_data": null}], "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862060546875, 103.10841369628906, 545.114990234375, 123.969970703125], "page": 12, "span": [0, 107], "__ref_s3_data": null}], "text": "- 9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 78.84821319580078, 286.3649597167969, 99.70977783203125], "page": 12, "span": [0, 110], "__ref_s3_data": null}], "text": "- 5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862060546875, 78.84840393066406, 545.1151733398438, 99.70997619628906], "page": 12, "span": [0, 118], "__ref_s3_data": null}], "text": "- 9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862060546875, 163.58441162109375, 545.1150512695312, 184.44696044921875], "page": 12, "span": [0, 101], "__ref_s3_data": null}], "text": "- 9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862060546875, 127.3694076538086, 545.1150512695312, 160.18597412109375], "page": 12, "span": [0, 117], "__ref_s3_data": null}], "text": "- 9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.8620300292969, 332.00836181640625, 545.1151733398438, 400.6898498535156], "page": 12, "span": [0, 311], "__ref_s3_data": null}], "text": "- 8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.8620300292969, 224.06141662597656, 545.1151733398438, 328.6089172363281], "page": 12, "span": [0, 503], "__ref_s3_data": null}], "text": "- 9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 536.2962036132812, 545.1151733398438, 581.0687866210938], "page": 12, "span": [0, 183], "__ref_s3_data": null}], "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 512.0361938476562, 545.114990234375, 532.8977661132812], "page": 12, "span": [0, 91], "__ref_s3_data": null}], "text": "- 6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [292.6310729980469, 48.96040725708008, 302.5936584472656, 57.86697006225586], "page": 12, "span": [0, 2], "__ref_s3_data": null}], "text": "12", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [50.11199951171875, 707.8850708007812, 88.84658813476562, 716.7916259765625], "page": 13, "span": [0, 10], "__ref_s3_data": null}], "text": "phan cell.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 683.9750366210938, 286.3649597167969, 704.8366088867188], "page": 13, "span": [0, 76], "__ref_s3_data": null}], "text": "9f. Otherwise create a new structural cell and match it wit the orphan cell.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 660.2941284179688, 286.364990234375, 680.8369140625], "page": 13, "span": [0, 97], "__ref_s3_data": null}], "text": "Aditional images with examples of TableFormer predictions and post-processing can be found below.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/8"}, {"name": "Table", "type": "table", "$ref": "#/tables/9"}, {"name": "Table", "type": "table", "$ref": "#/tables/10"}, {"prov": [{"bbox": [63.340999603271484, 281.0370788574219, 273.1334228515625, 289.9436340332031], "page": 13, "span": [0, 52], "__ref_s3_data": null}], "text": "Figure 8: Example of a table with multi-line header.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/11"}, {"prov": [{"bbox": [292.6309814453125, 48.960079193115234, 302.59356689453125, 57.866641998291016], "page": 13, "span": [0, 2], "__ref_s3_data": null}], "text": "13", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/12"}, {"name": "Table", "type": "table", "$ref": "#/tables/13"}, {"name": "Table", "type": "table", "$ref": "#/tables/14"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/12"}, {"prov": [{"bbox": [308.86199951171875, 464.54010009765625, 545.1151123046875, 485.4016418457031], "page": 13, "span": [0, 67], "__ref_s3_data": null}], "text": "Figure 9: Example of a table with big empty distance between cells.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/15"}, {"name": "Table", "type": "table", "$ref": "#/tables/16"}, {"name": "Table", "type": "table", "$ref": "#/tables/17"}, {"name": "Table", "type": "table", "$ref": "#/tables/18"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/13"}, {"prov": [{"bbox": [312.3429870605469, 102.60006713867188, 541.63232421875, 111.50663757324219], "page": 13, "span": [0, 55], "__ref_s3_data": null}], "text": "Figure 10: Example of a complex table with empty cells.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/19"}, {"name": "Table", "type": "table", "$ref": "#/tables/20"}, {"prov": [{"bbox": [50.11199951171875, 414.36810302734375, 286.3650817871094, 435.2296447753906], "page": 14, "span": [0, 61], "__ref_s3_data": null}], "text": "Figure 11: Simple table with different style and empty cells.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/14"}, {"name": "Table", "type": "table", "$ref": "#/tables/21"}, {"name": "Table", "type": "table", "$ref": "#/tables/22"}, {"name": "Table", "type": "table", "$ref": "#/tables/23"}, {"prov": [{"bbox": [54.61899948120117, 111.27507781982422, 281.85589599609375, 120.181640625], "page": 14, "span": [0, 56], "__ref_s3_data": null}], "text": "Figure 12: Simple table predictions and post processing.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/15"}, {"prov": [{"bbox": [292.6309814453125, 48.96007537841797, 302.59356689453125, 57.86663818359375], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "14", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/24"}, {"name": "Table", "type": "table", "$ref": "#/tables/25"}, {"name": "Table", "type": "table", "$ref": "#/tables/26"}, {"prov": [{"bbox": [315.7900085449219, 411.4090881347656, 538.1852416992188, 420.3156433105469], "page": 14, "span": [0, 55], "__ref_s3_data": null}], "text": "Figure 13: Table predictions example on colorful table.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/16"}, {"name": "Table", "type": "table", "$ref": "#/tables/27"}, {"name": "Table", "type": "table", "$ref": "#/tables/28"}, {"name": "Table", "type": "table", "$ref": "#/tables/29"}, {"prov": [{"bbox": [344.9849853515625, 99.54707336425781, 508.9893493652344, 108.45364379882812], "page": 14, "span": [0, 40], "__ref_s3_data": null}], "text": "Figure 14: Example with multi-line text.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/30"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/17"}, {"name": "Table", "type": "table", "$ref": "#/tables/31"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/18"}, {"name": "Table", "type": "table", "$ref": "#/tables/32"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/19"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/20"}, {"prov": [{"bbox": [84.23300170898438, 138.7420654296875, 252.24224853515625, 147.64862060546875], "page": 15, "span": [0, 41], "__ref_s3_data": null}], "text": "Figure 15: Example with triangular table.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/33"}, {"prov": [{"bbox": [292.6309814453125, 48.9600944519043, 302.59356689453125, 57.86665725708008], "page": 15, "span": [0, 2], "__ref_s3_data": null}], "text": "15", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/34"}, {"name": "Table", "type": "table", "$ref": "#/tables/35"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/21"}, {"name": "Table", "type": "table", "$ref": "#/tables/36"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/22"}, {"prov": [{"bbox": [308.8619689941406, 118.20308685302734, 545.1151123046875, 139.0646514892578], "page": 15, "span": [0, 106], "__ref_s3_data": null}], "text": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/37"}, {"prov": [{"bbox": [50.11199951171875, 262.80108642578125, 545.1138305664062, 283.6626281738281], "page": 16, "span": [0, 153], "__ref_s3_data": null}], "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/23"}, {"prov": [{"bbox": [292.6309814453125, 48.960079193115234, 302.59356689453125, 57.866641998291016], "page": 16, "span": [0, 2], "__ref_s3_data": null}], "text": "16", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}], "figures": [{"prov": [{"bbox": [315.65362548828125, 489.19854736328125, 537.1475219726562, 563.2765502929688], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [314.78173828125, 381.9505615234375, 539.1802978515625, 453.9347229003906], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [315.7172546386719, 295.9709777832031, 536.835693359375, 358.176513671875], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [312.10369873046875, 541.39013671875, 550.38916015625, 713.5591430664062], "page": 3, "span": [0, 104], "__ref_s3_data": null}], "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [74.30538940429688, 608.2989501953125, 519.9801025390625, 714.0888061523438], "page": 5, "span": [0, 212], "__ref_s3_data": null}], "text": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [53.033172607421875, 284.3311767578125, 285.3731384277344, 534.3345947265625], "page": 5, "span": [0, 745], "__ref_s3_data": null}], "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [49.97501754760742, 604.4212646484375, 301.6349182128906, 688.2876586914062], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [305.5844421386719, 611.374267578125, 554.8255615234375, 693.3489990234375], "page": 8, "span": [0, 79], "__ref_s3_data": null}], "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [51.73618698120117, 348.34197998046875, 211.83766174316406, 411.51922607421875], "page": 8, "span": [0, 397], "__ref_s3_data": null}], "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [216.76930236816406, 348.65301513671875, 375.7828674316406, 411.50933837890625], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [383.1363220214844, 349.2249755859375, 542.1131591796875, 410.7686767578125], "page": 8, "span": [0, 112], "__ref_s3_data": null}], "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [53.54228973388672, 644.4091186523438, 544.938232421875, 717.25146484375], "page": 12, "span": [0, 245], "__ref_s3_data": null}], "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [309.79150390625, 499.60601806640625, 425.9603271484375, 538.0946044921875], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [333.9573669433594, 126.5096435546875, 518.4768676757812, 198.8865966796875], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [51.1537971496582, 447.0933532714844, 282.8598937988281, 687.6914672851562], "page": 14, "span": [0, 61], "__ref_s3_data": null}], "text": "Figure 11: Simple table with different style and empty cells.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [50.404788970947266, 135.83905029296875, 177.05642700195312, 180.99615478515625], "page": 14, "span": [0, 56], "__ref_s3_data": null}], "text": "Figure 12: Simple table predictions and post processing.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [318.6332092285156, 432.9424133300781, 534.73583984375, 701.1157836914062], "page": 14, "span": [0, 55], "__ref_s3_data": null}], "text": "Figure 13: Table predictions example on colorful table.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [55.1163444519043, 542.66552734375, 279.370849609375, 655.7449951171875], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [54.28135299682617, 418.4728698730469, 279.2568359375, 531.7384033203125], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [55.423954010009766, 294.436279296875, 280.2310791015625, 407.4449462890625], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [50.64816665649414, 160.73651123046875, 319.91033935546875, 286.0196838378906], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [323.4686279296875, 327.739501953125, 525.9568481445312, 429.5491638183594], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [353.6920471191406, 156.22674560546875, 495.4288024902344, 304.594970703125], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [66.79946899414062, 293.8616027832031, 528.5564575195312, 538.3836669921875], "page": 16, "span": [0, 153], "__ref_s3_data": null}], "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [315.65362548828125, 489.19854736328125, 537.1475219726562, 563.2765502929688], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "type": "table", "payload": null, "#-cols": 2, "#-rows": 1, "data": [[{"bbox": [384.03289794921875, 529.1906127929688, 390.0376892089844, 539.321044921875], "spans": [[0, 0]], "text": "3", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [451.9457092285156, 546.5225219726562, 457.95050048828125, 556.6529541015625], "spans": [[0, 1]], "text": "1", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [315.7172546386719, 295.9709777832031, 536.835693359375, 358.176513671875], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. 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"row-span": [3, 4]}, {"bbox": [466.435791015625, 533.2503051757812, 482.5483093261719, 538.3154907226562], "spans": [[3, 3]], "text": "117.44", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [514.2906494140625, 533.2503051757812, 530.809814453125, 538.3154907226562], "spans": [[3, 4]], "text": "122.41", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [306.11492919921875, 525.3865966796875, 322.628662109375, 530.4517822265625], "spans": [[4, 0]], "text": "Vested", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": true, "row-span": [4, 5]}, {"bbox": [394.4322204589844, 525.3865966796875, 405.5362548828125, 530.4517822265625], "spans": [[4, 1]], "text": "(0. 5 )", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [427.70159912109375, 525.3865966796875, 438.8056335449219, 530.4517822265625], "spans": [[4, 2]], "text": "(0.1)", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [468.5553283691406, 525.3865966796875, 482.0704345703125, 530.4517822265625], "spans": [[4, 3]], "text": "87.08", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [516.0186157226562, 525.3865966796875, 529.5337524414062, 530.4517822265625], "spans": [[4, 4]], "text": "81.14", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 4, "row-header": false, "row-span": [4, 5]}], [{"bbox": [306.11492919921875, 517.2933349609375, 356.2477111816406, 522.3585205078125], "spans": [[5, 0]], "text": "Canceled or forfeited", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": true, "row-span": [5, 6]}, {"bbox": [394.4322204589844, 516.6153564453125, 405.5362548828125, 521.6805419921875], "spans": [[5, 1]], "text": "(0. 1 )", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [431.02801513671875, 516.6153564453125, 436.4280090332031, 521.6805419921875], "spans": [[5, 2]], "text": "-", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [465.83099365234375, 516.6153564453125, 482.3501281738281, 521.6805419921875], "spans": [[5, 3]], "text": "102.01", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [516.0186157226562, 516.6153564453125, 529.5337524414062, 521.6805419921875], "spans": [[5, 4]], "text": "92.18", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 5, "row-header": false, "row-span": [5, 6]}], [{"bbox": [306.11492919921875, 508.4490661621094, 373.3576354980469, 513.5142822265625], "spans": [[6, 0]], "text": "Nonvested on December 31", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 6, "row-header": true, "row-span": [6, 7]}, {"bbox": [396.2466125488281, 508.4490661621094, 403.75531005859375, 513.5142822265625], "spans": [[6, 1]], "text": "1.0", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [429.5159912109375, 508.4490661621094, 437.0246887207031, 513.5142822265625], "spans": [[6, 2]], "text": "0.3", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [463.7142028808594, 508.4490661621094, 484.7396545410156, 513.5142822265625], "spans": [[6, 3]], "text": "104.85 $", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [512.99462890625, 508.4490661621094, 534.0200805664062, 513.5142822265625], "spans": [[6, 4]], "text": "$ 104.51", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 6, "row-header": false, "row-span": [6, 7]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [84.0283203125, 577.606689453125, 239.1690673828125, 635.6664428710938], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [82.92001342773438, 500.716064453125, 239.1903533935547, 558.2236938476562], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [83.94786071777344, 424.0904235839844, 239.17135620117188, 482.9522705078125], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [83.31758117675781, 304.7430114746094, 248.87306213378906, 395.9864501953125], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 8: Example of a table with multi-line header.", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [310.3294372558594, 655.8525390625, 555.8338623046875, 690.8223876953125], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [309.9566345214844, 607.2774658203125, 555.7466430664062, 637.385498046875], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [309.9635314941406, 558.448486328125, 555.7054443359375, 596.2946166992188], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [309.79150390625, 499.60601806640625, 425.9603271484375, 538.0946044921875], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 9: Example of a table with big empty distance between cells.", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [335.2694091796875, 354.97760009765625, 490.08184814453125, 403.53253173828125], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [334.9334411621094, 289.2789001464844, 490.0914611816406, 338.0523681640625], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [335.2545471191406, 224.31207275390625, 490.22369384765625, 272.92431640625], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [333.9573669433594, 126.5096435546875, 518.4768676757812, 198.8865966796875], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 10: Example of a complex table with empty cells.", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [51.72642135620117, 447.7555236816406, 283.114013671875, 518.3907470703125], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [51.43488693237305, 300.17974853515625, 310.7267150878906, 338.51251220703125], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [50.86823654174805, 249.55401611328125, 310.6080017089844, 287.90374755859375], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [51.27280807495117, 200.086669921875, 311.0897216796875, 238.271484375], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [318.98101806640625, 577.3739013671875, 534.6228637695312, 630.765380859375], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [319.0057678222656, 512.142333984375, 534.408935546875, 565.8936767578125], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [328.13812255859375, 433.7275695800781, 523.8915405273438, 503.3182067871094], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [319.4707946777344, 314.05645751953125, 518.5693359375, 361.09698486328125], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [319.982666015625, 256.30419921875, 519.0963745117188, 302.7562561035156], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [319.82879638671875, 198.8935546875, 519.6065673828125, 245.5906982421875], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [319.0649719238281, 122.80792236328125, 533.7738647460938, 182.1591796875], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 14: Example with multi-line text.", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [55.1163444519043, 542.66552734375, 279.370849609375, 655.7449951171875], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [54.28135299682617, 418.4728698730469, 279.2568359375, 531.7384033203125], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [50.64816665649414, 160.73651123046875, 319.91033935546875, 286.0196838378906], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 15: Example with triangular table.", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [323.0059814453125, 569.088623046875, 525.95166015625, 670.452880859375], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [323.384765625, 447.9079284667969, 526.1268920898438, 550.027099609375], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [323.4686279296875, 327.739501953125, 525.9568481445312, 429.5491638183594], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [353.6920471191406, 156.22674560546875, 495.4288024902344, 304.594970703125], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}], "bitmaps": null, "equations": [], "footnotes": [], "page-dimensions": [{"height": 792.0, "page": 1, "width": 612.0}, {"height": 792.0, "page": 2, "width": 612.0}, {"height": 792.0, "page": 3, "width": 612.0}, {"height": 792.0, "page": 4, "width": 612.0}, {"height": 792.0, "page": 5, "width": 612.0}, {"height": 792.0, "page": 6, "width": 612.0}, {"height": 792.0, "page": 7, "width": 612.0}, {"height": 792.0, "page": 8, "width": 612.0}, {"height": 792.0, "page": 9, "width": 612.0}, {"height": 792.0, "page": 10, "width": 612.0}, {"height": 792.0, "page": 11, "width": 612.0}, {"height": 792.0, "page": 12, "width": 612.0}, {"height": 792.0, "page": 13, "width": 612.0}, {"height": 792.0, "page": 14, "width": 612.0}, {"height": 792.0, "page": 15, "width": 612.0}, {"height": 792.0, "page": 16, "width": 612.0}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null}
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/2203.01017v2.md b/tests/data/groundtruth/docling_v1/2203.01017v2.md
index 75b3a97e..6fe64db5 100644
--- a/tests/data/groundtruth/docling_v1/2203.01017v2.md
+++ b/tests/data/groundtruth/docling_v1/2203.01017v2.md
@@ -12,14 +12,12 @@
 
 The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.
 
-Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.
 
-|    | 3   |
-|----|-----|
-|  2 |     |
+<!-- image -->
+
+Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.
 
 
-<!-- image -->
 
 - b. Red-annotation of bounding boxes, Blue-predictions by TableFormer
 
@@ -29,16 +27,16 @@ Tables organize valuable content in a concise and compact representation. This c
 - c. Structure predicted by TableFormer:
 
 
-
-| 0      | 1 2   |   1 |
-|--------|-------|-----|
-| 3 4    | 5 3   |   6 |
-| 9      | 10    |  11 |
-| 8 13 2 | 14    |  15 |
-| 17     | 18    |  19 |
+<!-- image -->
 
 Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.
-<!-- image -->
+
+| 0   |   1 | 1   |   2 1 |   2 1 |    |
+|-----|-----|-----|-------|-------|----|
+| 3   |   4 | 5 3 |     6 |     7 |    |
+| 8   |   9 | 10  |    11 |    12 | 2  |
+|     |  13 | 14  |    15 |    16 | 2  |
+|     |  17 | 18  |    19 |    20 | 2  |
 
 Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.
 
diff --git a/tests/data/groundtruth/docling_v1/2203.01017v2.pages.json b/tests/data/groundtruth/docling_v1/2203.01017v2.pages.json
index 420017c7..66f05a36 100644
--- a/tests/data/groundtruth/docling_v1/2203.01017v2.pages.json
+++ b/tests/data/groundtruth/docling_v1/2203.01017v2.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers.", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 157.37334999999996, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "IBM Research", "bbox": {"l": 262.918, "t": 160.63239, "r": 332.30597, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Abstract", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tables organize valuable content in a concise and com-", "bbox": {"l": 62.066978, "t": 241.39508, "r": 286.36493, "b": 249.98284999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "pact representation. This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "c.", "bbox": {"l": 315.56702, "t": 420.1828, "r": 324.81039, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Structure predicted by TableFormer:", "bbox": {"l": 329.4321, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "1", "bbox": {"l": 408.14752, "t": 342.82828, "r": 412.54001, "b": 351.61322, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "0", "bbox": {"l": 356.11011, "t": 341.57217, "r": 360.50259, "b": 350.35712, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 500.6777, "t": 340.93768, "r": 505.0701900000001, "b": 349.7226299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3", "bbox": {"l": 356.13382, "t": 351.74789, "r": 360.52631, "b": 360.53284, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "4", "bbox": {"l": 402.53992, "t": 355.8765, "r": 406.9324, "b": 364.66144, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "5", 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437.5936, "r": 465.88101, "b": 447.724, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3", "bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). 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This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549326539039612, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": 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Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9791521430015564, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). 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This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph\u2019s, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF\u2019s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. 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"end_col_offset_idx": 3, "text": "19", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 459.87621999999993, "t": 437.5936, "r": 465.88101, "b": 447.724, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "1", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 308.862, "t": 514.50037, "r": 545.11517, "b": 559.2729, "coord_origin": "TOPLEFT"}, "confidence": 0.9214621186256409, "cells": [{"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: \u2018PMC2944238 004 02\u2019."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9791521430015564, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). 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This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph\u2019s, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF\u2019s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"label": "list_item", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549326539039612, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. 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multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: \u2018PMC2944238 004 02\u2019."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9791521430015564, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}], "headers": [{"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8773146271705627, "cells": [{"id": 125, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022"}, {"label": "page_footer", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.8045889139175415, "cells": [{"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656872153282166, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838618636131287, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428719282150269, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820107817649841, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822155237197876, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843322038650513, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.980295717716217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953584432601929, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778082728385925, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656872153282166, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838618636131287, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428719282150269, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820107817649841, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822155237197876, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843322038650513, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \u201cimage-encoder \u2192 text-decoder\u201d (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \u201cimage-encoder \u2192 dual decoder\u201d (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.980295717716217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953584432601929, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778082728385925, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}], "body": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656872153282166, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838618636131287, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428719282150269, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820107817649841, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822155237197876, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843322038650513, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \u201cimage-encoder \u2192 text-decoder\u201d (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \u201cimage-encoder \u2192 dual decoder\u201d (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.980295717716217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953584432601929, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://github.com/IBM/SynthTabNet"}], "headers": [{"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778082728385925, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0", "bbox": {"l": 320.97653, 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542.76428, "b": 161.21349999999995, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "4K", "bbox": {"l": 532.5705, "t": 176.75800000000004, "r": 542.53577, "b": 184.04796999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "2K", "bbox": {"l": 532.14551, "t": 199.6463, "r": 542.11078, "b": 206.93628, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767215609550476, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "picture", "bbox": {"l": 312.10369873046875, "t": 78.44087219238281, "r": 550.38916015625, "b": 250.60989379882812, 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396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 87.21935999999994, "r": 286.36514, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9822595119476318, "cells": [{"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 308.862, "t": 267.83636, "r": 545.11511, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9667503833770752, "cells": [{"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879812359809875, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840090274810791, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.87156081199646, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": 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"coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"label": "caption", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 308.862, "t": 267.83636, "r": 545.11511, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9667503833770752, "cells": [{"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN\u2019s) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN\u2019s) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879812359809875, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "balance in the previous datasets."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \u201csimple\u201d when it does not contain row spans or column spans, otherwise it is \u201ccomplex\u201d. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840090274810791, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}, {"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.87156081199646, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}], "body": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767215609550476, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tag-decoder which is constrained to the 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"both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"label": "caption", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 308.862, "t": 267.83636, "r": 545.11511, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9667503833770752, "cells": [{"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN\u2019s) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN\u2019s) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879812359809875, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "balance in the previous datasets."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \u201csimple\u201d when it does not contain row spans or column spans, otherwise it is \u201ccomplex\u201d. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840090274810791, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}], "headers": [{"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.87156081199646, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3", "bbox": {"l": 417.85599, "t": 85.6684600000001, "r": 425.37775, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "3", "bbox": {"l": 449.89569, "t": 85.6684600000001, "r": 457.41745000000003, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "509k", "bbox": {"l": 476.401, "t": 85.9673499999999, "r": 496.3262, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PNG", "bbox": {"l": 512.63495, "t": 85.9673499999999, "r": 532.56012, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "FinTabNet", "bbox": {"l": 317.06, "t": 97.92236000000003, "r": 359.43094, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "3", "bbox": {"l": 417.85599, "t": 97.62347, "r": 425.37775, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 449.89569, "t": 97.62347, "r": 457.41745000000003, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "112k", "bbox": {"l": 476.401, "t": 97.92236000000003, "r": 496.3262, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PDF", "bbox": {"l": 513.46185, "t": 97.92236000000003, "r": 531.73328, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "TableBank", "bbox": {"l": 317.06, "t": 109.87836000000004, "r": 359.97888, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "3", "bbox": {"l": 417.85599, "t": 109.57947000000001, "r": 425.37775, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "7", "bbox": {"l": 450.81226, "t": 109.57947000000001, "r": 456.50091999999995, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "145k", "bbox": {"l": 476.401, "t": 109.87836000000004, "r": 496.3262, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "JPEG", "bbox": {"l": 511.25017999999994, "t": 109.87836000000004, "r": 533.94501, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Combined-Tabnet(*)", "bbox": {"l": 317.06, "t": 121.83336999999995, "r": 400.37723, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3", "bbox": {"l": 417.85599, "t": 121.53448000000003, "r": 425.37775, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3", "bbox": {"l": 449.89569, "t": 121.53448000000003, "r": 457.41745000000003, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "400k", "bbox": {"l": 476.401, "t": 121.83336999999995, "r": 496.3262, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "PNG", "bbox": {"l": 512.63495, "t": 121.83336999999995, "r": 532.56012, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Combined(**)", "bbox": {"l": 317.06, "t": 133.78839000000005, "r": 375.17184, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "3", "bbox": {"l": 417.85599, "t": 133.48950000000002, "r": 425.37775, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3", "bbox": {"l": 449.89569, "t": 133.48950000000002, "r": 457.41745000000003, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "500k", "bbox": {"l": 476.401, "t": 133.78839000000005, "r": 496.3262, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "PNG", "bbox": {"l": 512.63495, "t": 133.78839000000005, "r": 532.56012, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "SynthTabNet", "bbox": {"l": 317.06, "t": 145.74341000000004, "r": 369.39352, "b": 154.64995999999996, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "3", "bbox": {"l": 417.85599, "t": 145.44446000000005, "r": 425.37775, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3", "bbox": {"l": 449.89569, "t": 145.44446000000005, "r": 457.41745000000003, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "600k", "bbox": {"l": 476.401, "t": 145.74334999999996, "r": 496.3262, "b": 154.6499, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "PNG", "bbox": {"l": 512.63495, "t": 145.74334999999996, "r": 532.56012, "b": 154.6499, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "table", "bbox": {"l": 317.06, "t": 73.61437999999998, "r": 536.91437, "b": 154.65985, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 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286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.66138, "r": 545.11505, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9668342471122742, "cells": [{"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9791285991668701, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9049099087715149, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874394536018372, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466320276260376, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.988465428352356, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376940131187439, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786656498908997, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"8": {"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 317.06, "t": 73.61437999999998, "r": 536.91437, "b": 154.65985, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, 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"b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9614067077636719, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.9880395531654358, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.66138, "r": 545.11505, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9668342471122742, "cells": [{"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: Both \u201cCombined-Tabnet\u201d and \u201dCombinedTabnet\u201d are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9791285991668701, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9049099087715149, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874394536018372, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466320276260376, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.988465428352356, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376940131187439, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (\u2018 < td > \u2019) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to \u2018 < \u2019, \u2018rowspan=\u2019 or \u2018colspan=\u2019, with the number of spanning cells (attribute), and \u2018 > \u2019. The hidden state attached to \u2018 < \u2019 is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786656498908997, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}, {"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}], "body": [{"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 317.06, "t": 73.61437999999998, "r": 536.91437, "b": 154.65985, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, 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false, "row_header": false, "row_section": false}, {"bbox": {"l": 449.89569, "t": 145.44446000000005, "r": 457.41745000000003, "b": 154.65985, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 476.401, "t": 145.74334999999996, "r": 496.3262, "b": 154.6499, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "600k", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 512.63495, "t": 145.74334999999996, "r": 532.56012, "b": 154.6499, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "PNG", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9614067077636719, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.9880395531654358, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.66138, "r": 545.11505, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9668342471122742, "cells": [{"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: Both \u201cCombined-Tabnet\u201d and \u201dCombinedTabnet\u201d are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9791285991668701, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9049099087715149, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874394536018372, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466320276260376, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.988465428352356, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376940131187439, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (\u2018 < td > \u2019) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to \u2018 < \u2019, \u2018rowspan=\u2019 or \u2018colspan=\u2019, with the number of spanning cells (attribute), and \u2018 > \u2019. The hidden state attached to \u2018 < \u2019 is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786656498908997, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}], "headers": [{"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "1.", "bbox": {"l": 81.688072, "t": 122.43970000000002, "r": 84.927567, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Item", "bbox": {"l": 86.54731, "t": 122.43970000000002, "r": 93.026291, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Amount", "bbox": {"l": 102.50498, "t": 115.25214000000005, "r": 115.3461, "b": 118.44135000000006, 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represent the input image.", "bbox": {"l": 50.112015, "t": 539.85738, "r": 286.36508, "b": 548.76393, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": 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Afterwards, a transformer en-", "bbox": {"l": 50.112015, "t": 587.6783800000001, "r": 286.36511, "b": 596.58493, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder and decoder architecture is employed to produce fea-", "bbox": {"l": 50.112015, "t": 599.63338, "r": 286.36508, "b": 608.53993, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "tures that are received by a linear layer, and the", "bbox": {"l": 50.112015, "t": 611.58838, "r": 240.43756000000002, "b": 620.4949300000001, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Cell BBox", "bbox": {"l": 243.19801, "t": 611.46883, "r": 286.36597, "b": 620.4252, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Decoder. The linear layer is applied to the features to", "bbox": {"l": 50.112015, "t": 623.42482, "r": 286.36511, "b": 632.3812, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "predict the tags. Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "forming classification, and adding an adaptive pooling", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 523.05786, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "layer", "bbox": {"l": 525.19983, "t": 249.53441999999995, "r": 545.11505, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "of size 28*28. 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318.32092, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "extensive experimentation, the", "bbox": {"l": 308.86194, "t": 321.36934999999994, "r": 432.35833999999994, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Structure Decoder", "bbox": {"l": 435.81995000000006, "t": 321.45901, "r": 510.29041, "b": 330.04678, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "is", "bbox": {"l": 513.97797, "t": 321.36934999999994, "r": 520.62305, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "mod-", "bbox": {"l": 524.08008, "t": 321.36934999999994, "r": 545.11115, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "eled as a transformer encoder with two encoder layers", "bbox": {"l": 308.86197, "t": 333.32434, "r": 527.76013, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "and", "bbox": {"l": 530.729, "t": 333.32434, "r": 545.11499, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "a transformer decoder made from a 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This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "picture", "bbox": {"l": 74.30538940429688, "t": 77.91117095947266, "r": 519.9801025390625, "b": 183.70108032226562, "coord_origin": "TOPLEFT"}, "confidence": 0.9296937584877014, "cells": [], "children": [{"id": 34, "label": "text", "bbox": {"l": 331.03699, "t": 78.55980999999997, "r": 352.12589, "b": 84.30042000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 24, "text": "BBoxes", "bbox": {"l": 331.03699, "t": 78.55980999999997, "r": 352.12589, "b": 84.30042000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 251.76939000000002, "t": 80.93096999999989, "r": 266.39557, "b": 86.67156999999997, "coord_origin": 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Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. 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The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 417.11426, "r": 545.11511, "b": 545.57271, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"id": 169, "text": "The transformer encoder receives an encoded", "bbox": {"l": 320.81696, "t": 417.11426, "r": 515.49609, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "image", "bbox": {"l": 520.7663, "t": 417.11426, "r": 545.11487, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "from the", "bbox": {"l": 308.86197, "t": 429.0692399999999, "r": 343.72107, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "CNN Backbone Network", "bbox": {"l": 347.03796, "t": 429.15891, "r": 446.45471000000003, "b": 437.74667, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "and refines it", "bbox": {"l": 449.93996999999996, "t": 429.0692399999999, "r": 503.06055000000003, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "through", "bbox": {"l": 506.37808, "t": 429.0692399999999, "r": 537.3717, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a", "bbox": {"l": 540.68927, "t": 429.0692399999999, "r": 545.11267, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "multi-head dot-product attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "caption", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 50.112, "t": 527.7828099999999, "r": 286.36597, "b": 680.27094, "coord_origin": "TOPLEFT"}, "confidence": 0.8913399577140808, "cells": [{"id": 107, "text": "Figure 4: Given an input image of a table, the", "bbox": {"l": 50.112, "t": 527.90237, "r": 229.78752, "b": 536.80893, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Encoder", "bbox": {"l": 231.787, "t": 527.7828099999999, "r": 267.76196, "b": 536.7392, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "pro-", "bbox": {"l": 269.76401, "t": 527.90237, "r": 286.36169, "b": 536.80893, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "duces fixed-length features that represent the input image.", "bbox": {"l": 50.112015, "t": 539.85738, "r": 286.36508, "b": 548.76393, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "Structure", "bbox": {"l": 245.59502, "t": 563.64882, "r": 286.362, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Decoder", "bbox": {"l": 50.112015, "t": 575.60382, "r": 85.519089, "b": 584.5602, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "receives \u2018tokenized tags\u2019 of the HTML code that", "bbox": {"l": 88.623016, "t": 575.7233699999999, "r": 286.36072, "b": 584.6299300000001, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "represent the table structure. Afterwards, a transformer en-", "bbox": {"l": 50.112015, "t": 587.6783800000001, "r": 286.36511, "b": 596.58493, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder and decoder architecture is employed to produce fea-", "bbox": {"l": 50.112015, "t": 599.63338, "r": 286.36508, "b": 608.53993, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "tures that are received by a linear layer, and the", "bbox": {"l": 50.112015, "t": 611.58838, "r": 240.43756000000002, "b": 620.4949300000001, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Cell BBox", "bbox": {"l": 243.19801, "t": 611.46883, "r": 286.36597, "b": 620.4252, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Decoder. The linear layer is applied to the features to", "bbox": {"l": 50.112015, "t": 623.42482, "r": 286.36511, "b": 632.3812, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "predict the tags. Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives \u2018tokenized tags\u2019 of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (\u2018 < td > \u2019, \u2018 < \u2019) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > \u2019 and \u2018 < \u2019 HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}, {"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}, "confidence": 0.8719567656517029, "cells": [{"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}], "body": [{"label": "picture", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "picture", "bbox": {"l": 74.30538940429688, "t": 77.91117095947266, "r": 519.9801025390625, "b": 183.70108032226562, "coord_origin": "TOPLEFT"}, "confidence": 0.9296937584877014, "cells": [], "children": [{"id": 34, "label": "text", "bbox": {"l": 331.03699, "t": 78.55980999999997, "r": 352.12589, "b": 84.30042000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 24, "text": "BBoxes", "bbox": {"l": 331.03699, "t": 78.55980999999997, "r": 352.12589, "b": 84.30042000000003, 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354.18588, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "ers that comprise mainly of multi-head attention and", "bbox": {"l": 308.86197, "t": 357.23532, "r": 524.51245, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "feed", "bbox": {"l": 527.96948, "t": 357.23532, "r": 545.11511, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "forward layers.", "bbox": {"l": 308.86197, "t": 369.19031000000007, "r": 370.39096, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "This configuration uses fewer layers", "bbox": {"l": 377.44449, "t": 369.19031000000007, "r": 526.91339, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "and", "bbox": {"l": 530.72906, "t": 369.19031000000007, "r": 545.11505, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "heads in comparison to networks applied to other", "bbox": {"l": 308.86197, "t": 381.14529000000005, "r": 505.46395999999993, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "problems", "bbox": {"l": 508.03430000000003, "t": 381.14529000000005, "r": 545.11511, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "(e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d),", "bbox": {"l": 308.86197, "t": 393.10028, "r": 517.68799, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "some-", "bbox": {"l": 520.76642, "t": 393.10028, "r": 545.11499, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "thing which we relate to the simplicity of table images.", "bbox": {"l": 308.86197, "t": 405.05526999999995, "r": 528.01935, "b": 413.96182, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 417.11426, "r": 545.11511, "b": 545.57271, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"id": 169, "text": "The transformer encoder receives an encoded", "bbox": {"l": 320.81696, "t": 417.11426, "r": 515.49609, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "image", "bbox": {"l": 520.7663, "t": 417.11426, "r": 545.11487, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "from the", "bbox": {"l": 308.86197, "t": 429.0692399999999, "r": 343.72107, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "CNN Backbone Network", "bbox": {"l": 347.03796, "t": 429.15891, "r": 446.45471000000003, "b": 437.74667, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "and refines it", "bbox": {"l": 449.93996999999996, "t": 429.0692399999999, "r": 503.06055000000003, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "through", "bbox": {"l": 506.37808, "t": 429.0692399999999, "r": 537.3717, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a", "bbox": {"l": 540.68927, "t": 429.0692399999999, "r": 545.11267, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "multi-head dot-product attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "caption", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 50.112, "t": 527.7828099999999, "r": 286.36597, "b": 680.27094, "coord_origin": "TOPLEFT"}, "confidence": 0.8913399577140808, "cells": [{"id": 107, "text": "Figure 4: Given an input image of a table, the", "bbox": {"l": 50.112, "t": 527.90237, "r": 229.78752, "b": 536.80893, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Encoder", "bbox": {"l": 231.787, "t": 527.7828099999999, "r": 267.76196, "b": 536.7392, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "pro-", "bbox": {"l": 269.76401, "t": 527.90237, "r": 286.36169, "b": 536.80893, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "duces fixed-length features that represent the input image.", "bbox": {"l": 50.112015, "t": 539.85738, "r": 286.36508, "b": 548.76393, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "Structure", "bbox": {"l": 245.59502, "t": 563.64882, "r": 286.362, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Decoder", "bbox": {"l": 50.112015, "t": 575.60382, "r": 85.519089, "b": 584.5602, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "receives \u2018tokenized tags\u2019 of the HTML code that", "bbox": {"l": 88.623016, "t": 575.7233699999999, "r": 286.36072, "b": 584.6299300000001, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "represent the table structure. Afterwards, a transformer en-", "bbox": {"l": 50.112015, "t": 587.6783800000001, "r": 286.36511, "b": 596.58493, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder and decoder architecture is employed to produce fea-", "bbox": {"l": 50.112015, "t": 599.63338, "r": 286.36508, "b": 608.53993, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "tures that are received by a linear layer, and the", "bbox": {"l": 50.112015, "t": 611.58838, "r": 240.43756000000002, "b": 620.4949300000001, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Cell BBox", "bbox": {"l": 243.19801, "t": 611.46883, "r": 286.36597, "b": 620.4252, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Decoder. The linear layer is applied to the features to", "bbox": {"l": 50.112015, "t": 623.42482, "r": 286.36511, "b": 632.3812, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "predict the tags. Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives \u2018tokenized tags\u2019 of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (\u2018 < td > \u2019, \u2018 < \u2019) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > \u2019 and \u2018 < \u2019 HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}], "headers": [{"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}, "confidence": 0.8719567656517029, "cells": [{"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423347115516663, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, 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"text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880596995353699, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441831469535828, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Transformer Encoder consists of two \u201cTransformer Encoder Layers\u201d, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \u201cTransformer Decoder Layers\u201d with similar input and output dimensions as the \u201cTransformer Encoder Layers\u201d. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a \u2019caching\u2019 technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423347115516663, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 \u2212 \u03bb ) l$_{box}$ (1)"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880596995353699, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441831469535828, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}], "body": [{"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Transformer Encoder consists of two \u201cTransformer Encoder Layers\u201d, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \u201cTransformer Decoder Layers\u201d with similar input and output dimensions as the \u201cTransformer Encoder Layers\u201d. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a \u2019caching\u2019 technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423347115516663, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 \u2212 \u03bb ) l$_{box}$ (1)"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880596995353699, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441831469535828, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}], "headers": [{"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 247.74349999999998, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "All", "bbox": {"l": 264.54044, "t": 426.66736, "r": 277.27264, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "EDD", "bbox": {"l": 81.612, "t": 443.62436, "r": 102.08514, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "PTN", "bbox": {"l": 134.87206, "t": 443.62436, "r": 153.69141, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "91.1", "bbox": {"l": 176.56554, "t": 443.62436, "r": 194.00009, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "88.7", "bbox": {"l": 220.82938000000001, "t": 443.62436, "r": 238.26393, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "89.9", "bbox": {"l": 262.18414, "t": 443.62436, "r": 279.61868, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "GTE", "bbox": {"l": 82.165001, "t": 455.58035, "r": 101.5323, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PTN", "bbox": {"l": 134.86716, "t": 455.58035, "r": 153.68651, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "-", "bbox": {"l": 183.62411, "t": 455.58035, "r": 186.94167, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "-", "bbox": {"l": 227.88795000000002, "t": 455.58035, "r": 231.20551, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "93.01", "bbox": {"l": 259.69855, "t": 455.58035, "r": 282.11441, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 468.13336, "r": 117.38329000000002, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PTN", "bbox": {"l": 134.86766, "t": 468.13336, "r": 153.68701, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "98.5", "bbox": {"l": 176.57111, "t": 468.13336, "r": 194.00566, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "95.0", "bbox": {"l": 220.83495, "t": 468.13336, "r": 238.26950000000002, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "96.75", "bbox": {"l": 259.698, "t": 468.01379, "r": 282.11386, "b": 476.97018, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "EDD", "bbox": {"l": 81.612, "t": 483.32635, "r": 102.08514, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "FTN", "bbox": {"l": 134.87206, "t": 483.32635, "r": 153.69141, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "88.4", "bbox": {"l": 176.56554, "t": 483.32635, "r": 194.00009, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "92.08", "bbox": {"l": 218.33870999999996, "t": 483.32635, "r": 240.75455999999997, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "90.6", "bbox": {"l": 262.18411, "t": 483.32635, "r": 279.61865, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "GTE", "bbox": {"l": 82.165001, "t": 495.28134, "r": 101.5323, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "FTN", "bbox": {"l": 134.86716, "t": 495.28134, "r": 153.68651, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "-", "bbox": {"l": 183.62411, "t": 495.28134, "r": 186.94167, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "-", "bbox": {"l": 227.88795000000002, "t": 495.28134, "r": 231.20551, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "87.14", "bbox": {"l": 259.69855, "t": 495.28134, "r": 282.11441, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "GTE (FT)", "bbox": {"l": 71.789001, "t": 507.23633, "r": 111.90838999999998, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "FTN", "bbox": {"l": 134.86221, "t": 507.23633, "r": 153.68156, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "-", "bbox": {"l": 183.62914, "t": 507.23633, "r": 186.94669, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "-", "bbox": {"l": 227.89297, "t": 507.23633, "r": 231.21053000000003, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "91.02", "bbox": {"l": 259.6936, "t": 507.23633, "r": 282.10947, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 519.1913099999999, "r": 117.38329000000002, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "FTN", "bbox": {"l": 134.86766, "t": 519.1913099999999, "r": 153.68701, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "97.5", "bbox": {"l": 176.57111, "t": 519.1913099999999, "r": 194.00566, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "96.0", "bbox": {"l": 220.83495, "t": 519.1913099999999, "r": 238.26950000000002, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "96.8", "bbox": {"l": 262.189, "t": 519.0717500000001, "r": 279.62354, "b": 528.02814, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "EDD", "bbox": {"l": 81.612, "t": 536.49837, "r": 102.08514, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "TB", "bbox": {"l": 137.91064, "t": 536.49837, "r": 150.64285, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "86.0", "bbox": {"l": 176.56554, "t": 536.49837, "r": 194.00009, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "-", "bbox": {"l": 227.89285, "t": 536.49837, "r": 231.21040000000002, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "86.0", "bbox": {"l": 262.18411, "t": 536.49837, "r": 279.61865, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 548.45436, "r": 117.38329000000002, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "TB", "bbox": {"l": 137.90625, "t": 548.45436, "r": 150.63846, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "89.6", "bbox": {"l": 176.57111, "t": 548.45436, "r": 194.00566, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "-", "bbox": {"l": 227.88845999999998, "t": 548.45436, "r": 231.20601, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "89.6", "bbox": {"l": 262.189, "t": 548.3348100000001, "r": 279.62354, "b": 557.2911799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 568.00237, "r": 117.38329000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "STN", "bbox": {"l": 134.86766, "t": 568.00237, "r": 153.68701, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "96.9", "bbox": {"l": 176.57111, "t": 568.00237, "r": 194.00566, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "95.7", "bbox": {"l": 220.83495, "t": 568.00237, "r": 238.26950000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "96.7", "bbox": {"l": 262.1897, "t": 568.00237, "r": 279.62424, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 270.62134000000003, "r": 377.00076, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "PubTabNet", "bbox": {"l": 393.69809, "t": 270.62134000000003, "r": 438.28073, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "79.2", "bbox": {"l": 455.63559, "t": 270.62134000000003, "r": 473.07013, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "82.7", "bbox": {"l": 498.16592, "t": 270.62134000000003, "r": 515.60046, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 282.57631999999995, "r": 377.86331, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "PubTabNet", "bbox": {"l": 393.69388, "t": 282.57631999999995, "r": 438.27652, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "82.1", "bbox": {"l": 455.63101, "t": 282.45676, "r": 473.06555000000003, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "86.8", "bbox": {"l": 498.1713, "t": 282.45676, "r": 515.60583, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 294.53131, "r": 377.86331, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "SynthTabNet", "bbox": {"l": 389.81842, "t": 294.53131, "r": 442.15194999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "87.7", "bbox": {"l": 455.63135, "t": 294.53131, "r": 473.06589, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "-", "bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "Table 3:", "bbox": {"l": 308.862, "t": 316.44931, "r": 341.49951, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Cell Bounding Box detection results on PubTab-", "bbox": {"l": 348.60284, "t": 316.44931, "r": 545.11517, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "Net, and FinTabNet. PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Model", "bbox": {"l": 358.01099, "t": 552.23337, "r": 384.02335, "b": 561.1399200000001, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "TEDS", "bbox": {"l": 449.03400000000005, "t": 546.25537, "r": 473.94049000000007, "b": 555.16193, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "Simple", "bbox": {"l": 408.50598, "t": 558.21037, "r": 436.73999, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Complex", "bbox": {"l": 448.6951, "t": 558.21037, "r": 485.07849, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "All", "bbox": {"l": 499.3848, "t": 558.21037, "r": 512.117, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "Tabula", "bbox": {"l": 357.68201, "t": 575.16736, "r": 384.3519, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "78.0", "bbox": {"l": 413.90097, "t": 575.16736, "r": 431.33550999999994, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "57.8", "bbox": {"l": 458.16479000000004, "t": 575.16736, "r": 475.59933000000007, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "67.9", "bbox": {"l": 497.0289, "t": 575.16736, "r": 514.46344, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "Traprange", "bbox": {"l": 350.72299, "t": 587.12236, "r": 391.31064, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "60.8", "bbox": {"l": 413.90582, "t": 587.12236, "r": 431.34036, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "49.9", "bbox": {"l": 458.16965, "t": 587.12236, "r": 475.60419, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "55.4", "bbox": {"l": 497.03374999999994, "t": 587.12236, "r": 514.46832, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Camelot", "bbox": {"l": 354.13599, "t": 599.07835, "r": 387.89923, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "80.0", "bbox": {"l": 413.90161, "t": 599.07835, "r": 431.33615, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "66.0", "bbox": {"l": 458.16544, "t": 599.07835, "r": 475.59998, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "73.0", "bbox": {"l": 497.02954000000005, "t": 599.07835, "r": 514.46411, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Acrobat Pro", "bbox": {"l": 346.55899, "t": 611.03336, "r": 395.47534, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "68.9", "bbox": {"l": 413.90616, "t": 611.03336, "r": 431.34069999999997, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "61.8", "bbox": {"l": 458.16998000000007, "t": 611.03336, "r": 475.60452, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "65.3", "bbox": {"l": 497.03409, "t": 611.03336, "r": 514.46863, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "EDD", "bbox": {"l": 360.78101, "t": 622.9883600000001, "r": 381.25415, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "91.2", "bbox": {"l": 413.90158, "t": 622.9883600000001, "r": 431.33612, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "85.4", "bbox": {"l": 458.16541, "t": 622.9883600000001, "r": 475.59995000000004, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "88.3", "bbox": {"l": 497.0295100000001, "t": 622.9883600000001, "r": 514.46405, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "TableFormer", "bbox": {"l": 345.483, "t": 634.94336, "r": 396.5513, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "95.4", "bbox": {"l": 413.90616, "t": 634.94336, "r": 431.34069999999997, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "90.1", "bbox": {"l": 458.16998000000007, "t": 634.94336, "r": 475.60452, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "93.6", "bbox": {"l": 497.03400000000005, "t": 634.82381, "r": 514.46857, "b": 643.78018, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "section_header", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}, "confidence": 0.9554283022880554, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500426650047302, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735332727432251, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855114221572876, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "table", "bbox": {"l": 326.79501, "t": 253.66436999999996, "r": 527.2276, "b": 303.43787, "coord_origin": "TOPLEFT"}, "confidence": 0.9691707491874695, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. 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Datasets and Metrics"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "our Cell BBox Decoder accuracy for cells with a class label of \u2018content\u2019 only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we\u2019ve integrated TableFormer\u2019s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"label": "formula", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500426650047302, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 \u2212 EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735332727432251, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855114221572876, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "table", "bbox": {"l": 326.79501, "t": 253.66436999999996, "r": 527.2276, "b": 303.43787, "coord_origin": "TOPLEFT"}, "confidence": 0.9691707491874695, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 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"start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "87.7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "-", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 308.862, "t": 316.44931, "r": 545.11517, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9519907236099243, "cells": [{"id": 162, "text": "Table 3:", "bbox": {"l": 308.862, "t": 316.44931, "r": 341.49951, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Cell Bounding Box detection results on PubTab-", "bbox": {"l": 348.60284, "t": 316.44931, "r": 545.11517, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "Net, and FinTabNet. PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 66.315002, "t": 414.71237, "r": 282.11441, "b": 576.90892, "coord_origin": "TOPLEFT"}, "confidence": 0.989250659942627, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 247.74349999999998, "b": 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"end_row_offset_idx": 7, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "93.6", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 15, "page_no": 6, "cluster": {"id": 15, "label": "text", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 613.29492, "coord_origin": "TOPLEFT"}, "confidence": 0.7209141850471497, "cells": [{"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"label": "text", "id": 16, "page_no": 6, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433366537094116, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "FT: Model was trained on PubTabNet then finetuned."}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854632616043091, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "caption", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 308.862, "t": 656.86136, "r": 545.11517, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9541405439376831, "cells": [{"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}, {"label": "page_footer", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}, "confidence": 0.8787976503372192, "cells": [{"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}], "body": [{"label": "section_header", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}, "confidence": 0.9554283022880554, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.3. Datasets and Metrics"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "our Cell BBox Decoder accuracy for cells with a class label of \u2018content\u2019 only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we\u2019ve integrated TableFormer\u2019s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"label": "formula", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500426650047302, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 \u2212 EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735332727432251, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855114221572876, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "table", "bbox": {"l": 326.79501, "t": 253.66436999999996, "r": 527.2276, "b": 303.43787, "coord_origin": "TOPLEFT"}, "confidence": 0.9691707491874695, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 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"bbox": {"l": 498.1713, "t": 282.45676, "r": 515.60583, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 294.53131, "r": 377.86331, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "SynthTabNet", "bbox": {"l": 389.81842, "t": 294.53131, "r": 442.15194999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "87.7", "bbox": {"l": 455.63135, "t": 294.53131, "r": 473.06589, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "-", "bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}], "children": [{"id": 73, "label": "text", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 74, "label": "text", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 75, "label": "text", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 76, "label": "text", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 77, 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 66.315002, "t": 414.71237, "r": 282.11441, "b": 576.90892, "coord_origin": "TOPLEFT"}, "confidence": 0.989250659942627, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 247.74349999999998, "b": 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"b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433366537094116, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "FT: Model was trained on PubTabNet then finetuned."}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854632616043091, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "caption", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 308.862, "t": 656.86136, "r": 545.11517, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9541405439376831, "cells": [{"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}], "headers": [{"label": "page_footer", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}, "confidence": 0.8787976503372192, "cells": [{"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "b.", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 62.219952, "b": 216.10645, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Structure predicted by TableFormer, with superimposed matched PDF cell text:", "bbox": {"l": 66.424026, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Japanese language (previously unseen by TableFormer):", "bbox": {"l": 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"TOPLEFT"}}, {"id": 180, "text": "Predicted Structure", "bbox": {"l": 384.35437, "t": 381.77722, "r": 430.99261, "b": 386.44281, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "bbox": {"l": 328.78101, "t": 704.920792, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "8", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 20, "label": "list_item", "bbox": {"l": 53.286037, "t": 78.68756000000008, "r": 61.550289, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.5819774866104126, "cells": [{"id": 4, "text": "a.", "bbox": {"l": 53.286037, "t": 78.68756000000008, "r": 61.550289, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "list_item", "bbox": {"l": 65.682419, "t": 78.68756000000008, "r": 499.55563, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.5412202477455139, "cells": [{"id": 5, "text": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "bbox": {"l": 65.682419, "t": 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This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852355718612671, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. 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"coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 162, "text": "53", "bbox": {"l": 528.04962, "t": 432.04431, "r": 534.3689, "b": 438.36295, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}, "confidence": 0.9153732657432556, "cells": [{"id": 181, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table."}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436547756195068, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}, "confidence": 0.9561256170272827, "cells": [{"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.5. Qualitative Analysis"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875592589378357, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \u201cSynthTabNet\u201d a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852355718612671, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various \u201ccomplex\u201d tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}, "confidence": 0.9442476034164429, "cells": [{"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}, "confidence": 0.8318724036216736, "cells": [{"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "bbox": {"l": 328.78101, "t": 704.920792, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-"}, {"label": "page_footer", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.87098228931427, "cells": [{"id": 220, "text": "8", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8"}], "body": [{"label": "list_item", "id": 20, "page_no": 7, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 53.286037, "t": 78.68756000000008, "r": 61.550289, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.5819774866104126, "cells": [{"id": 4, "text": "a.", "bbox": {"l": 53.286037, "t": 78.68756000000008, "r": 61.550289, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a."}, {"label": "list_item", "id": 21, "page_no": 7, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 65.682419, "t": 78.68756000000008, "r": 499.55563, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.5412202477455139, "cells": [{"id": 5, "text": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "bbox": {"l": 65.682419, "t": 78.68756000000008, "r": 499.55563, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells"}, {"label": "section_header", "id": 29, "page_no": 7, "cluster": {"id": 29, "label": "section_header", "bbox": {"l": 53.811783000000005, "t": 94.28112999999996, "r": 284.34592, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.4644920825958252, "cells": [{"id": 2, "text": "Japanese language (previously unseen by TableFormer):", "bbox": {"l": 53.811783000000005, "t": 94.28112999999996, "r": 284.34592, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Japanese language (previously unseen by TableFormer):"}, {"label": "section_header", "id": 31, "page_no": 7, "cluster": {"id": 31, "label": "section_header", "bbox": {"l": 304.83081, "t": 94.28112999999996, "r": 431.09119, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.46289125084877014, "cells": [{"id": 3, "text": "Example table from FinTabNet:", "bbox": {"l": 304.83081, "t": 94.28112999999996, "r": 431.09119, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example table from FinTabNet:"}, {"label": "picture", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "picture", "bbox": {"l": 305.5844421386719, "t": 98.65103149414062, "r": 554.8255615234375, "b": 180.62570190429688, "coord_origin": "TOPLEFT"}, "confidence": 0.7697681784629822, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "picture", "bbox": {"l": 49.97501754760742, "t": 103.71235656738281, "r": 301.6349182128906, "b": 187.57875061035156, "coord_origin": "TOPLEFT"}, "confidence": 0.7873176336288452, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "caption", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}, "confidence": 0.5986899733543396, "cells": [{"id": 0, "text": "b.", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 62.219952, "b": 216.10645, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Structure predicted by TableFormer, with superimposed matched PDF cell text:", "bbox": {"l": 66.424026, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. 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"section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436547756195068, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}, "confidence": 0.9561256170272827, "cells": [{"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.5. Qualitative Analysis"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875592589378357, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \u201cSynthTabNet\u201d a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852355718612671, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various \u201ccomplex\u201d tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}, "confidence": 0.9442476034164429, "cells": [{"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}, "confidence": 0.8318724036216736, "cells": [{"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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In", "bbox": {"l": 70.031013, "t": 517.15948, "r": 265.62408, "b": 525.17545, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "2019", "bbox": {"l": 268.42902, "t": 517.24017, "r": 286.36182, "b": 524.96924, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "International Conference on Document Analysis and Recog-", "bbox": {"l": 70.031021, "t": 528.19916, "r": 286.36337, "b": 535.92822, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "nition (ICDAR)", "bbox": {"l": 70.031021, "t": 539.15718, "r": 125.25507999999999, "b": 546.88622, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ", pages 749-755. IEEE, 2019. 3", "bbox": {"l": 125.25402, "t": 539.07648, "r": 240.05083, "b": 547.09244, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[33] Wenyuan Xue, Qingyong Li, and Dacheng Tao. Res2tim: reconstruct syntactic structures from table images. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 749-755. 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Image-based table recognition: Data, model,"}], "headers": [{"label": "page_footer", "id": 0, "page_no": 9, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 292.63, "t": 734.1329920000001, "r": 302.59259, "b": 743.039555, "coord_origin": "TOPLEFT"}, "confidence": 0.9069585204124451, "cells": [{"id": 127, "text": "10", "bbox": {"l": 292.63, "t": 734.1329920000001, "r": 302.59259, "b": 743.039555, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}]}}, {"page_no": 10, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497079730033875, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596062302589417, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699996113777161, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568929672241211, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335852265357971, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.978333592414856, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986662030220032, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497079730033875, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Details on the datasets"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596062302589417, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699996113777161, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568929672241211, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335852265357971, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.978333592414856, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986662030220032, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}], "body": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497079730033875, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Details on the datasets"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596062302589417, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699996113777161, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568929672241211, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335852265357971, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.978333592414856, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}], "headers": [{"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986662030220032, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "FinTabNet", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table Bank", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Train", "bbox": {"l": 82.553436, "t": 141.27617999999995, "r": 94.976013, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Complex", "bbox": {"l": 63.03878399999999, "t": 101.10413000000005, "r": 85.290085, "b": 106.06133999999986, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Simple", "bbox": {"l": 67.76786, "t": 124.39531999999997, "r": 85.231277, "b": 129.35253999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Complex", "bbox": {"l": 227.55121, "t": 102.53992000000005, "r": 249.80251, "b": 107.49712999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Simple", "bbox": {"l": 232.19898999999998, "t": 126.98577999999986, "r": 249.66241, "b": 131.94299, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Simple", "bbox": {"l": 396.2337, "t": 114.04522999999995, "r": 413.69711, "b": 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"Train Test Val", "bbox": {"l": 410.19409, "t": 141.27617999999995, "r": 444.68915, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "100% 130K 5K", "bbox": {"l": 391.37341, "t": 85.73321999999996, "r": 432.6716599999999, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "10K", "bbox": {"l": 435.60571000000004, "t": 86.26140999999996, "r": 445.62414999999993, "b": 91.21862999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Complex", "bbox": {"l": 113.94921, "t": 141.28845, "r": 136.20052, "b": 146.24567000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Non", "bbox": {"l": 116.91554000000001, "t": 94.81853999999998, "r": 127.05433999999998, "b": 99.77575999999999, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Strict", "bbox": {"l": 113.3146, "t": 100.93853999999999, "r": 127.05298, "b": 105.89575000000002, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "HTML", "bbox": {"l": 112.94112, "t": 107.05853000000013, "r": 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93.07977000000005, "r": 299.37451, "b": 98.03698999999995, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Strict", "bbox": {"l": 285.63513, "t": 99.19976999999994, "r": 299.3735, "b": 104.15698000000009, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "HTML", "bbox": {"l": 285.26111, "t": 105.31975999999997, "r": 299.37537, "b": 110.27697999999998, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Strict", "bbox": {"l": 285.43109, "t": 120.38995, "r": 299.16946, "b": 125.34717, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HTML", "bbox": {"l": 285.05713, "t": 126.50995, "r": 299.17139, "b": 131.46716000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Simple", "bbox": {"l": 311.34592, "t": 141.71063000000004, "r": 328.80933, "b": 146.66785000000004, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "47K", "bbox": {"l": 299.58362, "t": 86.69353999999998, "r": 309.60205, "b": 91.65075999999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Simple", "bbox": {"l": 466.04077000000007, "t": 141.67169, "r": 483.50418, "b": 146.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Non", "bbox": {"l": 459.02151, "t": 93.76116999999999, "r": 469.16031000000004, "b": 98.71838000000002, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Strict", "bbox": {"l": 455.4209, "t": 99.88116000000002, "r": 469.15927000000005, "b": 104.83838000000003, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "HTML", "bbox": {"l": 455.04691, "t": 106.00116000000014, "r": 469.16115999999994, "b": 110.95836999999995, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "145K", "bbox": {"l": 467.39401, "t": 85.57239000000004, "r": 480.6545100000001, "b": 90.52959999999996, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Complex", "bbox": {"l": 160.37672, "t": 141.58385999999996, "r": 182.62802, "b": 146.54107999999997, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Contain", "bbox": {"l": 153.74265, "t": 94.86481000000003, "r": 173.32664, "b": 99.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Missing", "bbox": {"l": 154.50967, "t": 100.98479999999995, "r": 173.3246, "b": 105.94202000000007, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "bboxes", "bbox": {"l": 155.27162, "t": 107.10479999999995, "r": 173.32664, "b": 112.06200999999987, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Contain", "bbox": {"l": 326.41302, "t": 107.23248000000001, "r": 345.99701, "b": 112.18970000000002, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Missing", "bbox": {"l": 327.17972, "t": 113.35248000000001, "r": 345.99463, "b": 118.30969000000005, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "bboxes", "bbox": {"l": 327.94131, "t": 119.47247000000004, "r": 345.99634, "b": 124.42969000000005, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Dataset", "bbox": {"l": 488.9942, "t": 104.15374999999983, "r": 508.76384999999993, "b": 109.11095999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "doesn't", "bbox": {"l": 490.1893, "t": 110.27373999999998, "r": 508.76349000000005, "b": 115.2309600000001, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "provide", "bbox": {"l": 489.72009, "t": 116.39373999999998, "r": 508.76758, "b": 121.35095000000013, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "bboxes", "bbox": {"l": 490.71121, "t": 122.51373000000001, "r": 508.76624, "b": 127.47095000000002, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Simple", "bbox": {"l": 185.37759, "t": 141.71118, "r": 202.84102, "b": 146.66840000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "230K 280K", "bbox": {"l": 168.50357, "t": 86.13611000000003, "r": 197.52699, "b": 91.09331999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "65K", "bbox": {"l": 357.3768, "t": 85.99707000000001, "r": 367.39523, "b": 90.95428000000004, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Complex Simple", "bbox": {"l": 333.73151, "t": 141.62323000000004, "r": 374.92862, "b": 146.58043999999995, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "47K", "bbox": {"l": 345.69101, "t": 86.05591000000004, "r": 355.70944, "b": 91.01312000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Simple", "bbox": {"l": 508.54248, "t": 141.37683000000004, "r": 526.00592, "b": 146.33405000000005, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "145K", "bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "9e. 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Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.8942293524742126, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.96295565366745, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423391819000244, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767084717750549, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562006592750549, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347665309906006, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280701637268066, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126085042953491, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491711854934692, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587164521217346, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459475636482239, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716898918151855, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298409819602966, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584902882575989, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.759074866771698, "cells": [{"id": 181, "text": "9d. 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Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"label": "text", "id": 16, "page_no": 11, "cluster": {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.8942293524742126, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.96295565366745, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423391819000244, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767084717750549, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562006592750549, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347665309906006, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280701637268066, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126085042953491, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as \u201cgood\u201d ones and \u201cbad\u201d ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491711854934692, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587164521217346, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459475636482239, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716898918151855, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9b. Intersect the orphan\u2019s bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298409819602966, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } \u2212 min { x$_{c}$ } (4)"}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584902882575989, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "text", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.759074866771698, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9d. Intersect the orphan\u2019s bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.290222, "r": 286.36496, "b": 713.151787, "coord_origin": "TOPLEFT"}, "confidence": 0.9260510802268982, "cells": [{"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}, {"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126599431037903, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}], "body": [{"label": "picture", "id": 30, "page_no": 11, "cluster": {"id": 30, "label": "picture", "bbox": {"l": 53.54228973388672, "t": 74.74851989746094, "r": 544.938232421875, "b": 147.5908966064453, "coord_origin": "TOPLEFT"}, "confidence": 0.6033812761306763, "cells": [], "children": [{"id": 27, "label": "text", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 3, "text": "Table Bank", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "FinTabNet", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 66, "label": "text", "bbox": {"l": 467.39401, "t": 85.57239000000004, "r": 480.6545100000001, "b": 90.52959999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 40, "text": "145K", "bbox": {"l": 467.39401, "t": 85.57239000000004, "r": 480.6545100000001, "b": 90.52959999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 60.93763400000001, "t": 85.73321999999996, "r": 76.151443, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "100%", "bbox": {"l": 60.93763400000001, "t": 85.73321999999996, "r": 76.151443, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 40, "label": "text", "bbox": {"l": 226.69780000000003, "t": 85.73321999999996, "r": 241.91161, "b": 90.69042999999999, 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The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.96295565366745, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423391819000244, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767084717750549, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562006592750549, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347665309906006, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280701637268066, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126085042953491, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as \u201cgood\u201d ones and \u201cbad\u201d ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491711854934692, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587164521217346, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459475636482239, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716898918151855, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9b. Intersect the orphan\u2019s bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298409819602966, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } \u2212 min { x$_{c}$ } (4)"}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584902882575989, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "text", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.759074866771698, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9d. Intersect the orphan\u2019s bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.290222, "r": 286.36496, "b": 713.151787, "coord_origin": "TOPLEFT"}, "confidence": 0.9260510802268982, "cells": [{"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}], "headers": [{"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126599431037903, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "phan cell.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 88.846588, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9f. Otherwise create a new structural cell and match it", "bbox": {"l": 62.067001, "t": 87.16339000000005, "r": 286.36496, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "wit the orphan cell.", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 127.03322, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Aditional images with examples of TableFormer predic-", "bbox": {"l": 62.067001, "t": 111.16309000000001, "r": 286.36499, "b": 119.7508499999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tions and post-processing can be found below.", "bbox": {"l": 50.112, "t": 123.11810000000003, "r": 234.06139999999996, "b": 131.70587, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Figure 8: Example of a table with multi-line header.", "bbox": {"l": 63.341, "t": 502.05637, "r": 273.13342, "b": 510.96292, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Figure 9:", "bbox": {"l": 308.862, "t": 306.59836, "r": 345.63397, "b": 315.50491, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Example of a table with big empty distance be-", "bbox": {"l": 352.78711, "t": 306.59836, "r": 545.11511, "b": 315.50491, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "tween cells.", "bbox": {"l": 308.862, "t": 318.55334, "r": 355.89545, "b": 327.45990000000006, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Figure 10: Example of a complex table with empty cells.", "bbox": {"l": 312.34299, "t": 680.4933599999999, "r": 541.63232, "b": 689.39993, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "13", "bbox": {"l": 292.63098, "t": 734.133358, "r": 302.59357, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 88.846588, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.7545604109764099, "cells": [{"id": 0, "text": "phan cell.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 88.846588, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36496, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9170765280723572, "cells": [{"id": 1, "text": "9f. Otherwise create a new structural cell and match it", "bbox": {"l": 62.067001, "t": 87.16339000000005, "r": 286.36496, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "wit the orphan cell.", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 127.03322, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "table", "bbox": {"l": 310.3294372558594, "t": 101.17761993408203, "r": 555.8338623046875, "b": 136.14747619628906, "coord_origin": "TOPLEFT"}, "confidence": 0.7048211097717285, "cells": [], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 111.16309000000001, "r": 286.36499, "b": 131.70587, "coord_origin": "TOPLEFT"}, "confidence": 0.9454684257507324, "cells": [{"id": 3, "text": "Aditional images with examples of TableFormer predic-", "bbox": {"l": 62.067001, "t": 111.16309000000001, "r": 286.36499, "b": 119.7508499999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tions and post-processing can be found below.", "bbox": {"l": 50.112, "t": 123.11810000000003, "r": 234.06139999999996, "b": 131.70587, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "table", "bbox": {"l": 309.9566345214844, "t": 154.61447143554688, "r": 555.7466430664062, "b": 184.72254943847656, "coord_origin": "TOPLEFT"}, "confidence": 0.5642898082733154, "cells": [], "children": []}, {"id": 9, "label": "table", "bbox": {"l": 84.0283203125, "t": 156.3335418701172, "r": 239.1690673828125, "b": 214.39334106445312, "coord_origin": "TOPLEFT"}, "confidence": 0.8857285976409912, "cells": [], "children": []}, {"id": 21, "label": "table", "bbox": {"l": 309.9635314941406, "t": 195.7053985595703, "r": 555.7054443359375, "b": 233.55148315429688, "coord_origin": "TOPLEFT"}, "confidence": 0.6602534651756287, "cells": [], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 82.92001342773438, "t": 233.7763214111328, "r": 239.1903533935547, "b": 291.283935546875, "coord_origin": "TOPLEFT"}, "confidence": 0.9155728220939636, "cells": [], "children": []}, {"id": 19, "label": "picture", "bbox": {"l": 309.79150390625, "t": 253.90536499023438, "r": 425.9603271484375, "b": 292.39398193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.6956613063812256, "cells": [], "children": []}, {"id": 37, "label": "table", "bbox": {"l": 309.79150390625, "t": 253.90536499023438, "r": 425.9603271484375, "b": 292.39398193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.5282703042030334, "cells": [], "children": []}, {"id": 11, "label": "caption", "bbox": {"l": 308.862, "t": 306.59836, "r": 545.11511, "b": 327.45990000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8773334622383118, "cells": [{"id": 6, "text": "Figure 9:", "bbox": {"l": 308.862, "t": 306.59836, "r": 345.63397, "b": 315.50491, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Example of a table with big empty distance be-", "bbox": {"l": 352.78711, "t": 306.59836, "r": 545.11511, "b": 315.50491, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "tween cells.", "bbox": {"l": 308.862, "t": 318.55334, "r": 355.89545, "b": 327.45990000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "table", "bbox": {"l": 83.94786071777344, "t": 309.0477294921875, "r": 239.17135620117188, "b": 367.9095764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9085132479667664, "cells": [], "children": []}, {"id": 12, "label": "table", "bbox": {"l": 335.2694091796875, "t": 388.46746826171875, "r": 490.08184814453125, "b": 437.02239990234375, "coord_origin": "TOPLEFT"}, "confidence": 0.8486074805259705, "cells": [], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 83.31758117675781, "t": 396.0135498046875, "r": 248.87306213378906, "b": 487.2569885253906, "coord_origin": "TOPLEFT"}, "confidence": 0.9613965749740601, "cells": [], "children": []}, {"id": 10, "label": "table", "bbox": {"l": 334.9334411621094, "t": 453.9476318359375, "r": 490.0914611816406, "b": 502.7210998535156, "coord_origin": "TOPLEFT"}, "confidence": 0.8815536499023438, "cells": [], "children": []}, {"id": 8, "label": "caption", "bbox": {"l": 63.341, "t": 502.05637, "r": 273.13342, "b": 510.96292, "coord_origin": "TOPLEFT"}, "confidence": 0.8971083164215088, "cells": [{"id": 5, "text": "Figure 8: Example of a table with multi-line header.", "bbox": {"l": 63.341, "t": 502.05637, "r": 273.13342, "b": 510.96292, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "table", "bbox": {"l": 335.2545471191406, "t": 519.07568359375, "r": 490.22369384765625, "b": 567.6879272460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9096733927726746, "cells": [], "children": []}, {"id": 13, "label": "picture", "bbox": {"l": 333.9573669433594, "t": 593.1134033203125, "r": 518.4768676757812, "b": 665.4903564453125, "coord_origin": "TOPLEFT"}, "confidence": 0.802356481552124, "cells": [], "children": []}, {"id": 30, "label": "table", "bbox": {"l": 333.9573669433594, "t": 593.1134033203125, "r": 518.4768676757812, "b": 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End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure."}, {"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 292.63098, "t": 734.133358, "r": 302.59357, "b": 743.039921, "coord_origin": "TOPLEFT"}, "confidence": 0.9134100675582886, "cells": [{"id": 2, "text": "16", "bbox": {"l": 292.63098, "t": 734.133358, "r": 302.59357, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "16"}], "body": [{"label": "picture", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 66.79946899414062, "t": 253.61631774902344, "r": 528.5564575195312, "b": 498.1383972167969, "coord_origin": "TOPLEFT"}, "confidence": 0.6913459897041321, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "caption", "bbox": {"l": 50.112, "t": 508.33737, "r": 545.11383, "b": 529.1989100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9624595642089844, "cells": [{"id": 0, "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post process-", "bbox": {"l": 50.112, "t": 508.33737, "r": 545.11383, "b": 517.24393, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "ing and prediction of structure.", "bbox": {"l": 50.112, "t": 520.2923599999999, "r": 173.23975, "b": 529.1989100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure."}], "headers": [{"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 292.63098, "t": 734.133358, "r": 302.59357, "b": 743.039921, "coord_origin": "TOPLEFT"}, "confidence": 0.9134100675582886, "cells": [{"id": 2, "text": "16", "bbox": {"l": 292.63098, "t": 734.133358, "r": 302.59357, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "16"}]}}]
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+[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers.", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 157.37334999999996, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "IBM Research", "bbox": {"l": 262.918, "t": 160.63239, "r": 332.30597, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Abstract", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tables organize valuable content in a concise and com-", "bbox": {"l": 62.066978, "t": 241.39508, "r": 286.36493, "b": 249.98284999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "pact representation. This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 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Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). 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This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549326539039612, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": 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Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9791521430015564, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). 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This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph\u2019s, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF\u2019s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. 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[{"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: \u2018PMC2944238 004 02\u2019."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9791521430015564, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}, {"label": "page_footer", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.8045889139175415, "cells": [{"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}], "body": [{"label": "section_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8868061304092407, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers.", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers."}, {"label": "section_header", "id": 13, "page_no": 0, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7586213946342468, "cells": [{"id": 1, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 157.37334999999996, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "IBM Research", "bbox": {"l": 262.918, "t": 160.63239, "r": 332.30597, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research"}, {"label": "key_value_region", "id": 25, "page_no": 0, "cluster": {"id": 25, "label": "key_value_region", "bbox": {"l": 208.60328674316406, "t": 175.79937744140625, "r": 379.33544921875, "b": 185.4495086669922, "coord_origin": "TOPLEFT"}, "confidence": 0.48547235131263733, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": [{"id": 7, "label": "text", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 378.73257, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9096333980560303, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9258671998977661, "cells": [{"id": 7, "text": "Abstract", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract"}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 315.56702, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}, "confidence": 0.6724023818969727, "cells": [{"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a. Picture of a table:"}, {"label": "picture", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.65362548828125, "t": 228.7234344482422, "r": 537.1475219726562, "b": 302.80145263671875, "coord_origin": "TOPLEFT"}, "confidence": 0.608779788017273, "cells": [], "children": [{"id": 62, "label": "text", "bbox": {"l": 451.9457100000001, "t": 235.34704999999997, "r": 457.95050000000003, "b": 245.47748, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 97, "text": "1", "bbox": {"l": 451.9457100000001, "t": 235.34704999999997, "r": 457.95050000000003, "b": 245.47748, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 70, "label": "text", "bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 105, "text": "3", "bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 65, "label": "text", "bbox": {"l": 331.19681, "t": 269.35266, "r": 337.2016, "b": 279.48308999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 100, "text": "2", "bbox": {"l": 331.19681, "t": 269.35266, "r": 337.2016, "b": 279.48308999999995, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "table", "id": 15, "page_no": 0, "cluster": {"id": 15, "label": "table", "bbox": {"l": 315.65362548828125, "t": 228.7234344482422, "r": 537.1475219726562, "b": 302.80145263671875, "coord_origin": "TOPLEFT"}, "confidence": 0.651587724685669, "cells": [{"id": 97, "text": "1", "bbox": {"l": 451.9457100000001, "t": 235.34704999999997, "r": 457.95050000000003, "b": 245.47748, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "2", "bbox": {"l": 331.19681, "t": 269.35266, "r": 337.2016, "b": 279.48308999999995, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3", "bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}}], "children": [{"id": 62, "label": "text", "bbox": {"l": 451.9457100000001, "t": 235.34704999999997, "r": 457.95050000000003, "b": 245.47748, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 97, "text": "1", "bbox": {"l": 451.9457100000001, "t": 235.34704999999997, "r": 457.95050000000003, "b": 245.47748, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 70, "label": "text", "bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 105, "text": "3", "bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 65, "label": "text", "bbox": {"l": 331.19681, "t": 269.35266, "r": 337.2016, "b": 279.48308999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 100, "text": "2", "bbox": {"l": 331.19681, "t": 269.35266, "r": 337.2016, "b": 279.48308999999995, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null, "otsl_seq": ["ecel", "ched", "ched", "ched", "ched", "nl", "rhed", "fcel", "fcel", "fcel", "fcel", "nl", "ucel", "fcel", "fcel", "fcel", "fcel", "nl", "ucel", "fcel", "fcel", "fcel", "fcel", "nl"], "num_rows": 1, "num_cols": 2, "table_cells": [{"bbox": {"l": 451.9457100000001, "t": 235.34704999999997, "r": 457.95050000000003, "b": 245.47748, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3", "column_header": true, "row_header": false, "row_section": false}]}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111977, "t": 241.39508, "r": 286.36511, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9838882088661194, "cells": [{"id": 8, "text": "Tables organize valuable content in a concise and com-", "bbox": {"l": 62.066978, "t": 241.39508, "r": 286.36493, "b": 249.98284999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "pact representation. This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph\u2019s, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF\u2019s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"label": "list_item", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549326539039612, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. 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[{"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: \u2018PMC2944238 004 02\u2019."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9791521430015564, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}], "headers": [{"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8773146271705627, "cells": [{"id": 125, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022"}, {"label": "page_footer", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.8045889139175415, "cells": [{"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656872153282166, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838618636131287, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428719282150269, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820107817649841, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822155237197876, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843322038650513, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.980295717716217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953584432601929, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778082728385925, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656872153282166, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838618636131287, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428719282150269, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820107817649841, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822155237197876, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843322038650513, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \u201cimage-encoder \u2192 text-decoder\u201d (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \u201cimage-encoder \u2192 dual decoder\u201d (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.980295717716217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953584432601929, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778082728385925, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}], "body": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656872153282166, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838618636131287, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428719282150269, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820107817649841, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822155237197876, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843322038650513, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \u201cimage-encoder \u2192 text-decoder\u201d (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \u201cimage-encoder \u2192 dual decoder\u201d (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.980295717716217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953584432601929, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://github.com/IBM/SynthTabNet"}], "headers": [{"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778082728385925, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0", "bbox": {"l": 320.97653, "t": 233.42296999999996, "r": 324.79254, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "20", "bbox": {"l": 410.483, "t": 233.42296999999996, "r": 418.11319, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "40", "bbox": {"l": 500.84949, "t": 233.42296999999996, "r": 508.47968000000003, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "10", "bbox": {"l": 365.29999, "t": 233.42296999999996, "r": 372.93018, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "30", "bbox": {"l": 455.66626, "t": 233.42296999999996, "r": 463.29645, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "50", "bbox": {"l": 542.03528, "t": 233.42296999999996, "r": 549.66547, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "0", "bbox": {"l": 316.04474, "t": 230.44617000000005, "r": 319.86075, "b": 236.27819999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "2", "bbox": {"l": 312.62521, "t": 198.69073000000003, "r": 316.44122, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "0", "bbox": {"l": 316.43942, "t": 198.69073000000003, "r": 320.2554, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "4", "bbox": {"l": 313.14951, "t": 168.09795999999994, "r": 316.96552, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "0", "bbox": {"l": 316.96371, "t": 168.09795999999994, "r": 320.77969, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "6", "bbox": {"l": 312.92972, "t": 136.58771000000002, "r": 316.74573, "b": 142.41974000000005, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "0", "bbox": {"l": 316.74393, "t": 136.58771000000002, "r": 320.55991, "b": 142.41974000000005, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "8", "bbox": {"l": 312.48227, "t": 105.60175000000004, "r": 316.29828, "b": 111.43377999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "0", "bbox": {"l": 316.29648, "t": 105.60175000000004, "r": 320.11246, "b": 111.43377999999996, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "1", "bbox": {"l": 312.48227, "t": 212.25922000000003, "r": 316.29828, "b": 218.09124999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "0", "bbox": {"l": 316.29648, "t": 212.25922000000003, "r": 320.11246, "b": 218.09124999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "3", "bbox": {"l": 313.07639, "t": 183.72198000000003, "r": 316.8924, "b": 189.55402000000004, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "0", "bbox": {"l": 316.89059, "t": 183.72198000000003, "r": 320.70657, "b": 189.55402000000004, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "5", "bbox": {"l": 312.76321, "t": 152.47400000000005, "r": 316.57922, "b": 158.30602999999996, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0", "bbox": {"l": 316.57742, "t": 152.47400000000005, "r": 320.3934, "b": 158.30602999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "7", "bbox": {"l": 312.19775, "t": 120.57050000000004, "r": 316.01376, "b": 126.40252999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0", "bbox": {"l": 316.01196, "t": 120.57050000000004, "r": 319.82794, "b": 126.40252999999996, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "9", "bbox": {"l": 312.8165, "t": 90.1087, "r": 316.63251, "b": 95.94073000000003, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "0", "bbox": {"l": 316.63071, "t": 90.1087, "r": 320.44669, "b": 95.94073000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "0", "bbox": {"l": 532.17426, "t": 222.72729000000004, "r": 536.94427, "b": 230.01727000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "10K", "bbox": {"l": 532.87952, "t": 108.26702999999986, "r": 547.61249, "b": 115.55700999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "8K", "bbox": {"l": 532.7735, "t": 130.78101000000004, "r": 542.73877, "b": 138.07097999999996, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "6K", "bbox": {"l": 532.79901, "t": 153.92352000000005, "r": 542.76428, "b": 161.21349999999995, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "4K", "bbox": {"l": 532.5705, "t": 176.75800000000004, "r": 542.53577, "b": 184.04796999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "2K", "bbox": {"l": 532.14551, "t": 199.6463, "r": 542.11078, "b": 206.93628, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767215609550476, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "picture", "bbox": {"l": 312.10369873046875, "t": 78.44087219238281, "r": 550.38916015625, "b": 250.60989379882812, 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For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 308.862, "t": 267.83636, "r": 545.11511, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9667503833770752, "cells": [{"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879812359809875, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840090274810791, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.87156081199646, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767215609550476, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tag-decoder which is constrained to the table-tags."}, {"label": "picture", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 312.10369873046875, "t": 78.44087219238281, "r": 550.38916015625, "b": 250.60989379882812, "coord_origin": "TOPLEFT"}, "confidence": 0.9746918082237244, "cells": [], "children": [{"id": 12, "label": "section_header", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, 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"coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"label": "caption", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 308.862, "t": 267.83636, "r": 545.11511, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9667503833770752, "cells": [{"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN\u2019s) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN\u2019s) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879812359809875, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "balance in the previous datasets."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \u201csimple\u201d when it does not contain row spans or column spans, otherwise it is \u201ccomplex\u201d. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840090274810791, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}, {"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.87156081199646, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}], "body": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767215609550476, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tag-decoder which is constrained to the table-tags."}, {"label": "picture", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 312.10369873046875, "t": 78.44087219238281, "r": 550.38916015625, "b": 250.60989379882812, "coord_origin": "TOPLEFT"}, "confidence": 0.9746918082237244, "cells": [], "children": [{"id": 12, "label": "section_header", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.5687219500541687, "cells": [{"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 312.8165, "t": 90.1087, "r": 316.63251, "b": 95.94073000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 90, "text": "9", "bbox": {"l": 312.8165, "t": 90.1087, "r": 316.63251, "b": 95.94073000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 38, 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"label": "text", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 87.21935999999994, "r": 286.36514, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9822595119476318, "cells": [{"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"label": "caption", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 308.862, "t": 267.83636, "r": 545.11511, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9667503833770752, "cells": [{"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN\u2019s) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN\u2019s) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879812359809875, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "balance in the previous datasets."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \u201csimple\u201d when it does not contain row spans or column spans, otherwise it is \u201ccomplex\u201d. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840090274810791, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}], "headers": [{"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.87156081199646, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3", "bbox": {"l": 417.85599, "t": 85.6684600000001, "r": 425.37775, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "3", "bbox": {"l": 449.89569, "t": 85.6684600000001, "r": 457.41745000000003, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "509k", "bbox": {"l": 476.401, "t": 85.9673499999999, "r": 496.3262, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PNG", "bbox": {"l": 512.63495, "t": 85.9673499999999, "r": 532.56012, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "FinTabNet", "bbox": {"l": 317.06, "t": 97.92236000000003, "r": 359.43094, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "3", "bbox": {"l": 417.85599, "t": 97.62347, "r": 425.37775, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 449.89569, "t": 97.62347, "r": 457.41745000000003, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "112k", "bbox": {"l": 476.401, "t": 97.92236000000003, "r": 496.3262, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PDF", "bbox": {"l": 513.46185, "t": 97.92236000000003, "r": 531.73328, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "TableBank", "bbox": {"l": 317.06, "t": 109.87836000000004, "r": 359.97888, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "3", "bbox": {"l": 417.85599, "t": 109.57947000000001, "r": 425.37775, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "7", "bbox": {"l": 450.81226, "t": 109.57947000000001, "r": 456.50091999999995, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "145k", "bbox": {"l": 476.401, "t": 109.87836000000004, "r": 496.3262, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "JPEG", "bbox": {"l": 511.25017999999994, "t": 109.87836000000004, "r": 533.94501, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Combined-Tabnet(*)", "bbox": {"l": 317.06, "t": 121.83336999999995, "r": 400.37723, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3", "bbox": {"l": 417.85599, "t": 121.53448000000003, "r": 425.37775, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3", "bbox": {"l": 449.89569, "t": 121.53448000000003, "r": 457.41745000000003, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "400k", "bbox": {"l": 476.401, "t": 121.83336999999995, "r": 496.3262, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "PNG", "bbox": {"l": 512.63495, "t": 121.83336999999995, "r": 532.56012, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Combined(**)", "bbox": {"l": 317.06, "t": 133.78839000000005, "r": 375.17184, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "3", "bbox": {"l": 417.85599, "t": 133.48950000000002, "r": 425.37775, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3", "bbox": {"l": 449.89569, "t": 133.48950000000002, "r": 457.41745000000003, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "500k", "bbox": {"l": 476.401, "t": 133.78839000000005, "r": 496.3262, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "PNG", "bbox": {"l": 512.63495, "t": 133.78839000000005, "r": 532.56012, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "SynthTabNet", "bbox": {"l": 317.06, "t": 145.74341000000004, "r": 369.39352, "b": 154.64995999999996, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "3", "bbox": {"l": 417.85599, "t": 145.44446000000005, "r": 425.37775, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3", "bbox": {"l": 449.89569, "t": 145.44446000000005, "r": 457.41745000000003, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "600k", "bbox": {"l": 476.401, "t": 145.74334999999996, "r": 496.3262, "b": 154.6499, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "PNG", "bbox": {"l": 512.63495, "t": 145.74334999999996, "r": 532.56012, "b": 154.6499, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. 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"r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.66138, "r": 545.11505, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9668342471122742, "cells": [{"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9791285991668701, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9049099087715149, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874394536018372, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466320276260376, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.988465428352356, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376940131187439, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786656498908997, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"8": {"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 310.6773681640625, "t": 73.19307708740234, "r": 542.958251953125, "b": 155.2208251953125, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": 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"id": 10, "page_no": 3, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9614067077636719, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.9880395531654358, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.66138, "r": 545.11505, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9668342471122742, "cells": [{"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: Both \u201cCombined-Tabnet\u201d and \u201dCombinedTabnet\u201d are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9791285991668701, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9049099087715149, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874394536018372, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466320276260376, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.988465428352356, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376940131187439, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (\u2018 < td > \u2019) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to \u2018 < \u2019, \u2018rowspan=\u2019 or \u2018colspan=\u2019, with the number of spanning cells (attribute), and \u2018 > \u2019. The hidden state attached to \u2018 < \u2019 is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786656498908997, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}, {"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}], "body": [{"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 310.6773681640625, "t": 73.19307708740234, "r": 542.958251953125, "b": 155.2208251953125, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, 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Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.66138, "r": 545.11505, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9668342471122742, "cells": [{"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: Both \u201cCombined-Tabnet\u201d and \u201dCombinedTabnet\u201d are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9791285991668701, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9049099087715149, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874394536018372, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466320276260376, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.988465428352356, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376940131187439, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (\u2018 < td > \u2019) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to \u2018 < \u2019, \u2018rowspan=\u2019 or \u2018colspan=\u2019, with the number of spanning cells (attribute), and \u2018 > \u2019. The hidden state attached to \u2018 < \u2019 is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786656498908997, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}], "headers": [{"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "1.", "bbox": {"l": 81.688072, "t": 122.43970000000002, "r": 84.927567, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Item", "bbox": {"l": 86.54731, "t": 122.43970000000002, "r": 93.026291, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Amount", "bbox": {"l": 102.50498, "t": 115.25214000000005, "r": 115.3461, "b": 118.44135000000006, 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"coord_origin": "TOPLEFT"}}, {"id": 30, "text": "provide full description of", "bbox": {"l": 431.1738, "t": 157.80517999999995, "r": 498.87753000000004, "b": 163.54578000000004, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "the table structure", "bbox": {"l": 440.5289, "t": 163.92516999999998, "r": 489.51827999999995, "b": 169.66576999999995, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Structure Tags", "bbox": {"l": 328.37479, "t": 178.25385000000006, "r": 367.72333, "b": 183.99445000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "BBoxes in sync", "bbox": {"l": 331.84451, "t": 123.90886999999998, "r": 373.67963, "b": 129.64948000000015, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "with tag sequence", "bbox": {"l": 331.84451, "t": 129.00885000000017, "r": 381.17786, "b": 134.74945000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Encoder", "bbox": {"l": 196.62633, "t": 88.11621000000002, "r": 219.42332, "b": 93.85681, "coord_origin": "TOPLEFT"}}, {"id": 36, 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The bounding boxes grabs the content from the PDF and inserts it in the structure.", "bbox": {"l": 50.111992, "t": 216.06035999999995, "r": 436.0134, "b": 224.96691999999996, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Input Image", "bbox": {"l": 74.253464, "t": 258.21472000000006, "r": 101.75846, "b": 264.17474000000004, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Tokenised Tags", "bbox": {"l": 122.29972, "t": 258.34520999999995, "r": 157.83972, "b": 264.30524, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Multi-Head Attention", "bbox": {"l": 78.549347, "t": 371.38579999999996, "r": 125.68359000000001, "b": 377.04782, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Add", "bbox": {"l": 78.513298, "t": 391.31857, "r": 84.644547, "b": 396.98059, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "& Normalisation", "bbox": {"l": 116.52705, "t": 391.31857, "r": 125.11079999999998, "b": 396.98059, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "Feed Forward Network", "bbox": 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represent the input image.", "bbox": {"l": 50.112015, "t": 539.85738, "r": 286.36508, "b": 548.76393, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": 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Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "forming classification, and adding an adaptive pooling", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 523.05786, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "layer", "bbox": {"l": 525.19983, "t": 249.53441999999995, "r": 545.11505, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "of size 28*28. 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This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. 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Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. 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The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 417.11426, "r": 545.11511, "b": 545.57271, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"id": 169, "text": "The transformer encoder receives an encoded", "bbox": {"l": 320.81696, "t": 417.11426, "r": 515.49609, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "image", "bbox": {"l": 520.7663, "t": 417.11426, "r": 545.11487, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "from the", "bbox": {"l": 308.86197, "t": 429.0692399999999, "r": 343.72107, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "CNN Backbone Network", "bbox": {"l": 347.03796, "t": 429.15891, "r": 446.45471000000003, "b": 437.74667, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "and refines it", "bbox": {"l": 449.93996999999996, "t": 429.0692399999999, "r": 503.06055000000003, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "through", "bbox": {"l": 506.37808, "t": 429.0692399999999, "r": 537.3717, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a", "bbox": {"l": 540.68927, "t": 429.0692399999999, "r": 545.11267, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "multi-head dot-product attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "caption", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 50.112, "t": 527.7828099999999, "r": 286.36597, "b": 680.27094, "coord_origin": "TOPLEFT"}, "confidence": 0.8913399577140808, "cells": [{"id": 107, "text": "Figure 4: Given an input image of a table, the", "bbox": {"l": 50.112, "t": 527.90237, "r": 229.78752, "b": 536.80893, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Encoder", "bbox": {"l": 231.787, "t": 527.7828099999999, "r": 267.76196, "b": 536.7392, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "pro-", "bbox": {"l": 269.76401, "t": 527.90237, "r": 286.36169, "b": 536.80893, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "duces fixed-length features that represent the input image.", "bbox": {"l": 50.112015, "t": 539.85738, "r": 286.36508, "b": 548.76393, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "Structure", "bbox": {"l": 245.59502, "t": 563.64882, "r": 286.362, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Decoder", "bbox": {"l": 50.112015, "t": 575.60382, "r": 85.519089, "b": 584.5602, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "receives \u2018tokenized tags\u2019 of the HTML code that", "bbox": {"l": 88.623016, "t": 575.7233699999999, "r": 286.36072, "b": 584.6299300000001, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "represent the table structure. Afterwards, a transformer en-", "bbox": {"l": 50.112015, "t": 587.6783800000001, "r": 286.36511, "b": 596.58493, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder and decoder architecture is employed to produce fea-", "bbox": {"l": 50.112015, "t": 599.63338, "r": 286.36508, "b": 608.53993, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "tures that are received by a linear layer, and the", "bbox": {"l": 50.112015, "t": 611.58838, "r": 240.43756000000002, "b": 620.4949300000001, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Cell BBox", "bbox": {"l": 243.19801, "t": 611.46883, "r": 286.36597, "b": 620.4252, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Decoder. The linear layer is applied to the features to", "bbox": {"l": 50.112015, "t": 623.42482, "r": 286.36511, "b": 632.3812, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "predict the tags. Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives \u2018tokenized tags\u2019 of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (\u2018 < td > \u2019, \u2018 < \u2019) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > \u2019 and \u2018 < \u2019 HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}, {"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}, "confidence": 0.8719567656517029, "cells": [{"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}], "body": [{"label": "picture", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "picture", "bbox": {"l": 74.30538940429688, "t": 77.91117095947266, "r": 519.9801025390625, "b": 183.70108032226562, "coord_origin": "TOPLEFT"}, "confidence": 0.9296937584877014, "cells": [], "children": [{"id": 34, "label": "text", "bbox": {"l": 331.03699, "t": 78.55980999999997, "r": 352.12589, "b": 84.30042000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 24, "text": "BBoxes", "bbox": {"l": 331.03699, "t": 78.55980999999997, "r": 352.12589, "b": 84.30042000000003, 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354.18588, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "ers that comprise mainly of multi-head attention and", "bbox": {"l": 308.86197, "t": 357.23532, "r": 524.51245, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "feed", "bbox": {"l": 527.96948, "t": 357.23532, "r": 545.11511, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "forward layers.", "bbox": {"l": 308.86197, "t": 369.19031000000007, "r": 370.39096, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "This configuration uses fewer layers", "bbox": {"l": 377.44449, "t": 369.19031000000007, "r": 526.91339, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "and", "bbox": {"l": 530.72906, "t": 369.19031000000007, "r": 545.11505, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "heads in comparison to networks applied to other", "bbox": {"l": 308.86197, "t": 381.14529000000005, "r": 505.46395999999993, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "problems", "bbox": {"l": 508.03430000000003, "t": 381.14529000000005, "r": 545.11511, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "(e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d),", "bbox": {"l": 308.86197, "t": 393.10028, "r": 517.68799, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "some-", "bbox": {"l": 520.76642, "t": 393.10028, "r": 545.11499, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "thing which we relate to the simplicity of table images.", "bbox": {"l": 308.86197, "t": 405.05526999999995, "r": 528.01935, "b": 413.96182, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 417.11426, "r": 545.11511, "b": 545.57271, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"id": 169, "text": "The transformer encoder receives an encoded", "bbox": {"l": 320.81696, "t": 417.11426, "r": 515.49609, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "image", "bbox": {"l": 520.7663, "t": 417.11426, "r": 545.11487, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "from the", "bbox": {"l": 308.86197, "t": 429.0692399999999, "r": 343.72107, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "CNN Backbone Network", "bbox": {"l": 347.03796, "t": 429.15891, "r": 446.45471000000003, "b": 437.74667, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "and refines it", "bbox": {"l": 449.93996999999996, "t": 429.0692399999999, "r": 503.06055000000003, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "through", "bbox": {"l": 506.37808, "t": 429.0692399999999, "r": 537.3717, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a", "bbox": {"l": 540.68927, "t": 429.0692399999999, "r": 545.11267, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "multi-head dot-product attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "caption", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 50.112, "t": 527.7828099999999, "r": 286.36597, "b": 680.27094, "coord_origin": "TOPLEFT"}, "confidence": 0.8913399577140808, "cells": [{"id": 107, "text": "Figure 4: Given an input image of a table, the", "bbox": {"l": 50.112, "t": 527.90237, "r": 229.78752, "b": 536.80893, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Encoder", "bbox": {"l": 231.787, "t": 527.7828099999999, "r": 267.76196, "b": 536.7392, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "pro-", "bbox": {"l": 269.76401, "t": 527.90237, "r": 286.36169, "b": 536.80893, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "duces fixed-length features that represent the input image.", "bbox": {"l": 50.112015, "t": 539.85738, "r": 286.36508, "b": 548.76393, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "Structure", "bbox": {"l": 245.59502, "t": 563.64882, "r": 286.362, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Decoder", "bbox": {"l": 50.112015, "t": 575.60382, "r": 85.519089, "b": 584.5602, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "receives \u2018tokenized tags\u2019 of the HTML code that", "bbox": {"l": 88.623016, "t": 575.7233699999999, "r": 286.36072, "b": 584.6299300000001, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "represent the table structure. Afterwards, a transformer en-", "bbox": {"l": 50.112015, "t": 587.6783800000001, "r": 286.36511, "b": 596.58493, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder and decoder architecture is employed to produce fea-", "bbox": {"l": 50.112015, "t": 599.63338, "r": 286.36508, "b": 608.53993, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "tures that are received by a linear layer, and the", "bbox": {"l": 50.112015, "t": 611.58838, "r": 240.43756000000002, "b": 620.4949300000001, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Cell BBox", "bbox": {"l": 243.19801, "t": 611.46883, "r": 286.36597, "b": 620.4252, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Decoder. The linear layer is applied to the features to", "bbox": {"l": 50.112015, "t": 623.42482, "r": 286.36511, "b": 632.3812, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "predict the tags. Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives \u2018tokenized tags\u2019 of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (\u2018 < td > \u2019, \u2018 < \u2019) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > \u2019 and \u2018 < \u2019 HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}], "headers": [{"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}, "confidence": 0.8719567656517029, "cells": [{"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423347115516663, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, 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"text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880596995353699, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441831469535828, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Transformer Encoder consists of two \u201cTransformer Encoder Layers\u201d, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \u201cTransformer Decoder Layers\u201d with similar input and output dimensions as the \u201cTransformer Encoder Layers\u201d. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a \u2019caching\u2019 technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423347115516663, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 \u2212 \u03bb ) l$_{box}$ (1)"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880596995353699, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441831469535828, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}], "body": [{"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Transformer Encoder consists of two \u201cTransformer Encoder Layers\u201d, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \u201cTransformer Decoder Layers\u201d with similar input and output dimensions as the \u201cTransformer Encoder Layers\u201d. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a \u2019caching\u2019 technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423347115516663, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 \u2212 \u03bb ) l$_{box}$ (1)"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880596995353699, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441831469535828, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}], "headers": [{"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 247.74349999999998, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "All", "bbox": {"l": 264.54044, "t": 426.66736, "r": 277.27264, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "EDD", "bbox": {"l": 81.612, "t": 443.62436, "r": 102.08514, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "PTN", "bbox": {"l": 134.87206, "t": 443.62436, "r": 153.69141, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "91.1", "bbox": {"l": 176.56554, "t": 443.62436, "r": 194.00009, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "88.7", "bbox": {"l": 220.82938000000001, "t": 443.62436, "r": 238.26393, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "89.9", "bbox": {"l": 262.18414, "t": 443.62436, "r": 279.61868, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "GTE", "bbox": {"l": 82.165001, "t": 455.58035, "r": 101.5323, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PTN", "bbox": {"l": 134.86716, "t": 455.58035, "r": 153.68651, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "-", "bbox": {"l": 183.62411, "t": 455.58035, "r": 186.94167, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "-", "bbox": {"l": 227.88795000000002, "t": 455.58035, "r": 231.20551, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "93.01", "bbox": {"l": 259.69855, "t": 455.58035, "r": 282.11441, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 468.13336, "r": 117.38329000000002, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PTN", "bbox": {"l": 134.86766, "t": 468.13336, "r": 153.68701, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "98.5", "bbox": {"l": 176.57111, "t": 468.13336, "r": 194.00566, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "95.0", "bbox": {"l": 220.83495, "t": 468.13336, "r": 238.26950000000002, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "96.75", "bbox": {"l": 259.698, "t": 468.01379, "r": 282.11386, "b": 476.97018, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "EDD", "bbox": {"l": 81.612, "t": 483.32635, "r": 102.08514, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "FTN", "bbox": {"l": 134.87206, "t": 483.32635, "r": 153.69141, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "88.4", "bbox": {"l": 176.56554, "t": 483.32635, "r": 194.00009, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "92.08", "bbox": {"l": 218.33870999999996, "t": 483.32635, "r": 240.75455999999997, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "90.6", "bbox": {"l": 262.18411, "t": 483.32635, "r": 279.61865, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "GTE", "bbox": {"l": 82.165001, "t": 495.28134, "r": 101.5323, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "FTN", "bbox": {"l": 134.86716, "t": 495.28134, "r": 153.68651, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "-", "bbox": {"l": 183.62411, "t": 495.28134, "r": 186.94167, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "-", "bbox": {"l": 227.88795000000002, "t": 495.28134, "r": 231.20551, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "87.14", "bbox": {"l": 259.69855, "t": 495.28134, "r": 282.11441, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "GTE (FT)", "bbox": {"l": 71.789001, "t": 507.23633, "r": 111.90838999999998, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "FTN", "bbox": {"l": 134.86221, "t": 507.23633, "r": 153.68156, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "-", "bbox": {"l": 183.62914, "t": 507.23633, "r": 186.94669, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "-", "bbox": {"l": 227.89297, "t": 507.23633, "r": 231.21053000000003, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "91.02", "bbox": {"l": 259.6936, "t": 507.23633, "r": 282.10947, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 519.1913099999999, "r": 117.38329000000002, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "FTN", "bbox": {"l": 134.86766, "t": 519.1913099999999, "r": 153.68701, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "97.5", "bbox": {"l": 176.57111, "t": 519.1913099999999, "r": 194.00566, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "96.0", "bbox": {"l": 220.83495, "t": 519.1913099999999, "r": 238.26950000000002, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "96.8", "bbox": {"l": 262.189, "t": 519.0717500000001, "r": 279.62354, "b": 528.02814, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "EDD", "bbox": {"l": 81.612, "t": 536.49837, "r": 102.08514, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "TB", "bbox": {"l": 137.91064, "t": 536.49837, "r": 150.64285, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "86.0", "bbox": {"l": 176.56554, "t": 536.49837, "r": 194.00009, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "-", "bbox": {"l": 227.89285, "t": 536.49837, "r": 231.21040000000002, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "86.0", "bbox": {"l": 262.18411, "t": 536.49837, "r": 279.61865, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 548.45436, "r": 117.38329000000002, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "TB", "bbox": {"l": 137.90625, "t": 548.45436, "r": 150.63846, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "89.6", "bbox": {"l": 176.57111, "t": 548.45436, "r": 194.00566, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "-", "bbox": {"l": 227.88845999999998, "t": 548.45436, "r": 231.20601, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "89.6", "bbox": {"l": 262.189, "t": 548.3348100000001, "r": 279.62354, "b": 557.2911799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 568.00237, "r": 117.38329000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "STN", "bbox": {"l": 134.86766, "t": 568.00237, "r": 153.68701, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "96.9", "bbox": {"l": 176.57111, "t": 568.00237, "r": 194.00566, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "95.7", "bbox": {"l": 220.83495, "t": 568.00237, "r": 238.26950000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "96.7", "bbox": {"l": 262.1897, "t": 568.00237, "r": 279.62424, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 270.62134000000003, "r": 377.00076, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "PubTabNet", "bbox": {"l": 393.69809, "t": 270.62134000000003, "r": 438.28073, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "79.2", "bbox": {"l": 455.63559, "t": 270.62134000000003, "r": 473.07013, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "82.7", "bbox": {"l": 498.16592, "t": 270.62134000000003, "r": 515.60046, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 282.57631999999995, "r": 377.86331, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "PubTabNet", "bbox": {"l": 393.69388, "t": 282.57631999999995, "r": 438.27652, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "82.1", "bbox": {"l": 455.63101, "t": 282.45676, "r": 473.06555000000003, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "86.8", "bbox": {"l": 498.1713, "t": 282.45676, "r": 515.60583, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 294.53131, "r": 377.86331, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "SynthTabNet", "bbox": {"l": 389.81842, "t": 294.53131, "r": 442.15194999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "87.7", "bbox": {"l": 455.63135, "t": 294.53131, "r": 473.06589, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "-", "bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "Table 3:", "bbox": {"l": 308.862, "t": 316.44931, "r": 341.49951, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Cell Bounding Box detection results on PubTab-", "bbox": {"l": 348.60284, "t": 316.44931, "r": 545.11517, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "Net, and FinTabNet. PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Model", "bbox": {"l": 358.01099, "t": 552.23337, "r": 384.02335, "b": 561.1399200000001, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "TEDS", "bbox": {"l": 449.03400000000005, "t": 546.25537, "r": 473.94049000000007, "b": 555.16193, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "Simple", "bbox": {"l": 408.50598, "t": 558.21037, "r": 436.73999, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Complex", "bbox": {"l": 448.6951, "t": 558.21037, "r": 485.07849, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "All", "bbox": {"l": 499.3848, "t": 558.21037, "r": 512.117, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "Tabula", "bbox": {"l": 357.68201, "t": 575.16736, "r": 384.3519, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "78.0", "bbox": {"l": 413.90097, "t": 575.16736, "r": 431.33550999999994, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "57.8", "bbox": {"l": 458.16479000000004, "t": 575.16736, "r": 475.59933000000007, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "67.9", "bbox": {"l": 497.0289, "t": 575.16736, "r": 514.46344, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "Traprange", "bbox": {"l": 350.72299, "t": 587.12236, "r": 391.31064, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "60.8", "bbox": {"l": 413.90582, "t": 587.12236, "r": 431.34036, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "49.9", "bbox": {"l": 458.16965, "t": 587.12236, "r": 475.60419, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "55.4", "bbox": {"l": 497.03374999999994, "t": 587.12236, "r": 514.46832, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Camelot", "bbox": {"l": 354.13599, "t": 599.07835, "r": 387.89923, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "80.0", "bbox": {"l": 413.90161, "t": 599.07835, "r": 431.33615, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "66.0", "bbox": {"l": 458.16544, "t": 599.07835, "r": 475.59998, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "73.0", "bbox": {"l": 497.02954000000005, "t": 599.07835, "r": 514.46411, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Acrobat Pro", "bbox": {"l": 346.55899, "t": 611.03336, "r": 395.47534, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "68.9", "bbox": {"l": 413.90616, "t": 611.03336, "r": 431.34069999999997, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "61.8", "bbox": {"l": 458.16998000000007, "t": 611.03336, "r": 475.60452, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "65.3", "bbox": {"l": 497.03409, "t": 611.03336, "r": 514.46863, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "EDD", "bbox": {"l": 360.78101, "t": 622.9883600000001, "r": 381.25415, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "91.2", "bbox": {"l": 413.90158, "t": 622.9883600000001, "r": 431.33612, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "85.4", "bbox": {"l": 458.16541, "t": 622.9883600000001, "r": 475.59995000000004, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "88.3", "bbox": {"l": 497.0295100000001, "t": 622.9883600000001, "r": 514.46405, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "TableFormer", "bbox": {"l": 345.483, "t": 634.94336, "r": 396.5513, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "95.4", "bbox": {"l": 413.90616, "t": 634.94336, "r": 431.34069999999997, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "90.1", "bbox": {"l": 458.16998000000007, "t": 634.94336, "r": 475.60452, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "93.6", "bbox": {"l": 497.03400000000005, "t": 634.82381, "r": 514.46857, "b": 643.78018, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "section_header", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}, "confidence": 0.9554283022880554, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500426650047302, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735332727432251, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855114221572876, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "table", "bbox": {"l": 308.40673828125, "t": 247.87644958496094, "r": 533.6420288085938, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691707491874695, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 270.62134000000003, "r": 377.00076, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "PubTabNet", "bbox": {"l": 393.69809, "t": 270.62134000000003, "r": 438.28073, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "79.2", "bbox": {"l": 455.63559, "t": 270.62134000000003, "r": 473.07013, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "82.7", "bbox": {"l": 498.16592, "t": 270.62134000000003, "r": 515.60046, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 282.57631999999995, "r": 377.86331, "b": 291.48288, 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. 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Datasets and Metrics"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "our Cell BBox Decoder accuracy for cells with a class label of \u2018content\u2019 only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we\u2019ve integrated TableFormer\u2019s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"label": "formula", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500426650047302, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 \u2212 EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735332727432251, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855114221572876, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "table", "bbox": {"l": 308.40673828125, "t": 247.87644958496094, "r": 533.6420288085938, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691707491874695, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 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303.43787, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "87.7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "-", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 308.862, "t": 316.44931, "r": 545.11517, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9519907236099243, "cells": [{"id": 162, "text": "Table 3:", "bbox": {"l": 308.862, "t": 316.44931, "r": 341.49951, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Cell Bounding Box detection results on PubTab-", "bbox": {"l": 348.60284, "t": 316.44931, "r": 545.11517, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "Net, and FinTabNet. PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 53.36848831176758, "t": 409.1356506347656, "r": 283.04437255859375, "b": 582.397705078125, "coord_origin": "TOPLEFT"}, "confidence": 0.989250659942627, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, 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"coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "93.6", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 15, "page_no": 6, "cluster": {"id": 15, "label": "text", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 613.29492, "coord_origin": "TOPLEFT"}, "confidence": 0.7209141850471497, "cells": [{"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"label": "text", "id": 16, "page_no": 6, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433366537094116, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "FT: Model was trained on PubTabNet then finetuned."}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854632616043091, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "caption", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 308.862, "t": 656.86136, "r": 545.11517, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9541405439376831, "cells": [{"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}, {"label": "page_footer", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}, "confidence": 0.8787976503372192, "cells": [{"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}], "body": [{"label": "section_header", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}, "confidence": 0.9554283022880554, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.3. Datasets and Metrics"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "our Cell BBox Decoder accuracy for cells with a class label of \u2018content\u2019 only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we\u2019ve integrated TableFormer\u2019s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"label": "formula", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500426650047302, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 \u2212 EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735332727432251, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855114221572876, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "table", "bbox": {"l": 308.40673828125, "t": 247.87644958496094, "r": 533.6420288085938, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691707491874695, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 270.62134000000003, "r": 377.00076, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "PubTabNet", "bbox": {"l": 393.69809, "t": 270.62134000000003, "r": 438.28073, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "79.2", "bbox": {"l": 455.63559, "t": 270.62134000000003, "r": 473.07013, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "82.7", "bbox": {"l": 498.16592, "t": 270.62134000000003, "r": 515.60046, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 282.57631999999995, "r": 377.86331, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "PubTabNet", "bbox": {"l": 393.69388, "t": 282.57631999999995, "r": 438.27652, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "82.1", "bbox": {"l": 455.63101, "t": 282.45676, "r": 473.06555000000003, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "86.8", "bbox": {"l": 498.1713, "t": 282.45676, "r": 515.60583, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 294.53131, "r": 377.86331, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "SynthTabNet", "bbox": {"l": 389.81842, "t": 294.53131, "r": 442.15194999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "87.7", "bbox": {"l": 455.63135, "t": 294.53131, "r": 473.06589, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "-", "bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}], "children": [{"id": 73, "label": "text", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}], 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Box detection results on PubTab-", "bbox": {"l": 348.60284, "t": 316.44931, "r": 545.11517, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "Net, and FinTabNet. PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 53.36848831176758, "t": 409.1356506347656, "r": 283.04437255859375, "b": 582.397705078125, "coord_origin": "TOPLEFT"}, "confidence": 0.989250659942627, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, 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"cluster": {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433366537094116, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "FT: Model was trained on PubTabNet then finetuned."}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854632616043091, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "caption", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 308.862, "t": 656.86136, "r": 545.11517, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9541405439376831, "cells": [{"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}], "headers": [{"label": "page_footer", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}, "confidence": 0.8787976503372192, "cells": [{"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "b.", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 62.219952, "b": 216.10645, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Structure predicted by TableFormer, with superimposed matched PDF cell text:", "bbox": {"l": 66.424026, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Japanese language (previously unseen by TableFormer):", "bbox": {"l": 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"TOPLEFT"}}, {"id": 180, "text": "Predicted Structure", "bbox": {"l": 384.35437, "t": 381.77722, "r": 430.99261, "b": 386.44281, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "bbox": {"l": 328.78101, "t": 704.920792, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "8", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 20, "label": "list_item", "bbox": {"l": 53.286037, "t": 78.68756000000008, "r": 61.550289, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.5819774866104126, "cells": [{"id": 4, "text": "a.", "bbox": {"l": 53.286037, "t": 78.68756000000008, "r": 61.550289, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "list_item", "bbox": {"l": 65.682419, "t": 78.68756000000008, "r": 499.55563, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.5412202477455139, "cells": [{"id": 5, "text": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "bbox": {"l": 65.682419, "t": 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This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852355718612671, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. 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"section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436547756195068, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}, "confidence": 0.9561256170272827, "cells": [{"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.5. Qualitative Analysis"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875592589378357, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \u201cSynthTabNet\u201d a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852355718612671, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various \u201ccomplex\u201d tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}, "confidence": 0.9442476034164429, "cells": [{"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}, "confidence": 0.8318724036216736, "cells": [{"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "bbox": {"l": 328.78101, "t": 704.920792, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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"section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436547756195068, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}, "confidence": 0.9561256170272827, "cells": [{"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.5. Qualitative Analysis"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875592589378357, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \u201cSynthTabNet\u201d a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852355718612671, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various \u201ccomplex\u201d tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}, "confidence": 0.9442476034164429, "cells": [{"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}, "confidence": 0.8318724036216736, "cells": [{"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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Winter Conference for Applications in Computer Vision (WACV) , 2021. 2, 3"}, {"label": "list_item", "id": 8, "page_no": 9, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112015, "t": 693.961502, "r": 286.36334, "b": 712.936462, "coord_origin": "TOPLEFT"}, "confidence": 0.838570237159729, "cells": [{"id": 106, "text": "[37]", "bbox": {"l": 50.112015, "t": 693.961502, "r": 66.506706, "b": 701.977463, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Xu", "bbox": {"l": 68.966896, "t": 693.961502, "r": 80.992294, "b": 701.977463, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Zhong,", "bbox": {"l": 89.062057, "t": 693.961502, "r": 114.71492999999998, "b": 701.977463, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Elaheh", "bbox": {"l": 124.24621000000002, "t": 693.961502, "r": 149.1459, "b": 701.977463, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "ShafieiBavani,", "bbox": {"l": 157.22462, "t": 693.961502, "r": 209.37321, "b": 701.977463, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "and", "bbox": {"l": 218.9045, "t": 693.961502, "r": 231.85196999999997, "b": 701.977463, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Antonio", "bbox": {"l": 239.93069, "t": 693.961502, "r": 269.32254, "b": 701.977463, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Ji-", "bbox": {"l": 277.3923, "t": 693.961502, "r": 286.3587, "b": 701.977463, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "meno Yepes. Image-based table recognition: Data, model,", "bbox": {"l": 70.031013, "t": 704.920502, "r": 286.36334, "b": 712.936462, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[37] Xu Zhong, Elaheh ShafieiBavani, and Antonio Jimeno Yepes. Image-based table recognition: Data, model,"}], "headers": [{"label": "page_footer", "id": 0, "page_no": 9, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 292.63, "t": 734.1329920000001, "r": 302.59259, "b": 743.039555, "coord_origin": "TOPLEFT"}, "confidence": 0.9069585204124451, "cells": [{"id": 127, "text": "10", "bbox": {"l": 292.63, "t": 734.1329920000001, "r": 302.59259, "b": 743.039555, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}]}}, {"page_no": 10, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497079730033875, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596062302589417, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699996113777161, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568929672241211, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335852265357971, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.978333592414856, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986662030220032, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497079730033875, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Details on the datasets"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596062302589417, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699996113777161, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568929672241211, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335852265357971, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.978333592414856, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986662030220032, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}], "body": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497079730033875, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Details on the datasets"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596062302589417, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699996113777161, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568929672241211, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335852265357971, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.978333592414856, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}], "headers": [{"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986662030220032, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "FinTabNet", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}}, {"id": 3, 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"text": "47K", "bbox": {"l": 345.69101, "t": 86.05591000000004, "r": 355.70944, "b": 91.01312000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Simple", "bbox": {"l": 508.54248, "t": 141.37683000000004, "r": 526.00592, "b": 146.33405000000005, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "145K", "bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 30, "label": "picture", "bbox": {"l": 53.54228973388672, "t": 74.74851989746094, "r": 544.938232421875, "b": 147.5908966064453, "coord_origin": "TOPLEFT"}, "confidence": 0.6033812761306763, "cells": [], "children": [{"id": 27, "label": "text", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 3, "text": "Table Bank", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "FinTabNet", "bbox": {"l": 289.5791, "t": 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Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.8942293524742126, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.96295565366745, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423391819000244, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767084717750549, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562006592750549, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347665309906006, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280701637268066, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126085042953491, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491711854934692, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587164521217346, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459475636482239, "cells": [{"id": 168, "text": "9a. 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The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.96295565366745, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423391819000244, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767084717750549, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562006592750549, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347665309906006, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280701637268066, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126085042953491, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as \u201cgood\u201d ones and \u201cbad\u201d ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491711854934692, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587164521217346, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459475636482239, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716898918151855, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9b. Intersect the orphan\u2019s bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298409819602966, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } \u2212 min { x$_{c}$ } (4)"}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584902882575989, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "text", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.759074866771698, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9d. Intersect the orphan\u2019s bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.290222, "r": 286.36496, "b": 713.151787, "coord_origin": "TOPLEFT"}, "confidence": 0.9260510802268982, "cells": [{"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}, {"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126599431037903, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}], "body": [{"label": "picture", "id": 30, "page_no": 11, "cluster": {"id": 30, "label": "picture", "bbox": {"l": 53.54228973388672, "t": 74.74851989746094, "r": 544.938232421875, "b": 147.5908966064453, "coord_origin": "TOPLEFT"}, "confidence": 0.6033812761306763, "cells": [], "children": [{"id": 27, "label": "text", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 1, "text": 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Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"label": "text", "id": 16, "page_no": 11, "cluster": {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.8942293524742126, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.96295565366745, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423391819000244, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767084717750549, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562006592750549, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347665309906006, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280701637268066, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126085042953491, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as \u201cgood\u201d ones and \u201cbad\u201d ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491711854934692, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587164521217346, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459475636482239, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716898918151855, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9b. Intersect the orphan\u2019s bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298409819602966, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } \u2212 min { x$_{c}$ } (4)"}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584902882575989, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "text", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.759074866771698, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9d. Intersect the orphan\u2019s bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.290222, "r": 286.36496, "b": 713.151787, "coord_origin": "TOPLEFT"}, "confidence": 0.9260510802268982, "cells": [{"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}], "headers": [{"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126599431037903, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "phan cell.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 88.846588, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9f. Otherwise create a new structural cell and match it", "bbox": {"l": 62.067001, "t": 87.16339000000005, "r": 286.36496, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "wit the orphan cell.", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 127.03322, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Aditional images with examples of TableFormer predic-", "bbox": {"l": 62.067001, "t": 111.16309000000001, "r": 286.36499, "b": 119.7508499999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tions and post-processing can be found below.", "bbox": {"l": 50.112, "t": 123.11810000000003, "r": 234.06139999999996, "b": 131.70587, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Figure 8: Example of a table with multi-line header.", "bbox": {"l": 63.341, "t": 502.05637, "r": 273.13342, "b": 510.96292, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Figure 9:", "bbox": {"l": 308.862, "t": 306.59836, "r": 345.63397, "b": 315.50491, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Example of a table with big empty distance be-", "bbox": {"l": 352.78711, "t": 306.59836, "r": 545.11511, "b": 315.50491, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "tween cells.", "bbox": {"l": 308.862, "t": 318.55334, "r": 355.89545, "b": 327.45990000000006, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Figure 10: Example of a complex table with empty cells.", "bbox": {"l": 312.34299, "t": 680.4933599999999, "r": 541.63232, "b": 689.39993, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "13", "bbox": {"l": 292.63098, "t": 734.133358, "r": 302.59357, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 88.846588, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.7545604109764099, "cells": [{"id": 0, "text": "phan cell.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 88.846588, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36496, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9170765280723572, "cells": [{"id": 1, "text": "9f. Otherwise create a new structural cell and match it", "bbox": {"l": 62.067001, "t": 87.16339000000005, "r": 286.36496, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "wit the orphan cell.", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 127.03322, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "table", "bbox": {"l": 310.3294372558594, "t": 101.17761993408203, "r": 555.8338623046875, "b": 136.14747619628906, "coord_origin": "TOPLEFT"}, "confidence": 0.7048211097717285, "cells": [], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 111.16309000000001, "r": 286.36499, "b": 131.70587, "coord_origin": "TOPLEFT"}, "confidence": 0.9454684257507324, "cells": [{"id": 3, "text": "Aditional images with examples of TableFormer predic-", "bbox": {"l": 62.067001, "t": 111.16309000000001, "r": 286.36499, "b": 119.7508499999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tions and post-processing can be 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\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/2206.01062.doctags.txt b/tests/data/groundtruth/docling_v1/2206.01062.doctags.txt
index e90c3224..5cb2e383 100644
--- a/tests/data/groundtruth/docling_v1/2206.01062.doctags.txt
+++ b/tests/data/groundtruth/docling_v1/2206.01062.doctags.txt
@@ -57,9 +57,9 @@
 <paragraph><location><page_3><loc_52><loc_11><loc_91><loc_20></location>The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,</paragraph>
 <caption><location><page_4><loc_9><loc_85><loc_91><loc_89></location>Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.</caption>
 <table>
-<location><page_4><loc_17><loc_63><loc_83><loc_82></location>
+<location><page_4><loc_16><loc_63><loc_84><loc_83></location>
 <caption>Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.</caption>
-<row_0><col_0><body></col_0><col_1><body></col_1><col_2><col_header>% of Total</col_2><col_3><col_header>% of Total</col_3><col_4><col_header>% of Total</col_4><col_5><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_5><col_6><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_6><col_7><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_7><col_8><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_8><col_9><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_9><col_10><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_10><col_11><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_11></row_0>
+<row_0><col_0><body></col_0><col_1><body></col_1><col_2><col_header>% of Total</col_2><col_3><col_header>% of Total</col_3><col_4><col_header>% of Total</col_4><col_5><col_header>% of Total</col_5><col_6><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_6><col_7><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_7><col_8><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_8><col_9><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_9><col_10><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_10><col_11><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_11></row_0>
 <row_1><col_0><col_header>class label</col_0><col_1><col_header>Count</col_1><col_2><col_header>Train</col_2><col_3><col_header>Test</col_3><col_4><col_header>Val</col_4><col_5><col_header>All</col_5><col_6><col_header>Fin</col_6><col_7><col_header>Man</col_7><col_8><col_header>Sci</col_8><col_9><col_header>Law</col_9><col_10><col_header>Pat</col_10><col_11><col_header>Ten</col_11></row_1>
 <row_2><col_0><row_header>Caption</col_0><col_1><body>22524</col_1><col_2><body>2.04</col_2><col_3><body>1.77</col_3><col_4><body>2.32</col_4><col_5><body>84-89</col_5><col_6><body>40-61</col_6><col_7><body>86-92</col_7><col_8><body>94-99</col_8><col_9><body>95-99</col_9><col_10><body>69-78</col_10><col_11><body>n/a</col_11></row_2>
 <row_3><col_0><row_header>Footnote</col_0><col_1><body>6318</col_1><col_2><body>0.60</col_2><col_3><body>0.31</col_3><col_4><body>0.58</col_4><col_5><body>83-91</col_5><col_6><body>n/a</col_6><col_7><body>100</col_7><col_8><body>62-88</col_8><col_9><body>85-94</col_9><col_10><body>n/a</col_10><col_11><body>82-97</col_11></row_3>
@@ -105,7 +105,7 @@
 <paragraph><location><page_5><loc_52><loc_10><loc_91><loc_31></location>Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted</paragraph>
 <caption><location><page_6><loc_9><loc_77><loc_48><loc_89></location>Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.</caption>
 <table>
-<location><page_6><loc_11><loc_56><loc_46><loc_75></location>
+<location><page_6><loc_10><loc_56><loc_47><loc_75></location>
 <caption>Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.</caption>
 <row_0><col_0><body></col_0><col_1><col_header>human</col_1><col_2><col_header>MRCNN</col_2><col_3><col_header>MRCNN</col_3><col_4><col_header>FRCNN</col_4><col_5><col_header>YOLO</col_5></row_0>
 <row_1><col_0><body></col_0><col_1><col_header>human</col_1><col_2><col_header>R50</col_2><col_3><col_header>R101</col_3><col_4><col_header>R101</col_4><col_5><col_header>v5x6</col_5></row_1>
@@ -137,7 +137,7 @@
 <paragraph><location><page_7><loc_9><loc_84><loc_48><loc_89></location>Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.</paragraph>
 <caption><location><page_7><loc_52><loc_84><loc_91><loc_89></location>Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.</caption>
 <table>
-<location><page_7><loc_14><loc_63><loc_43><loc_81></location>
+<location><page_7><loc_13><loc_63><loc_44><loc_81></location>
 <caption>Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.</caption>
 <row_0><col_0><col_header>Class-count</col_0><col_1><col_header>11</col_1><col_2><col_header>6</col_2><col_3><col_header>5</col_3><col_4><col_header>4</col_4></row_0>
 <row_1><col_0><row_header>Caption</col_0><col_1><body>68</col_1><col_2><body>Text</col_2><col_3><body>Text</col_3><col_4><body>Text</col_4></row_1>
@@ -153,8 +153,12 @@
 <row_11><col_0><row_header>Title</col_0><col_1><body>77</col_1><col_2><body>Sec.-h.</col_2><col_3><body>Sec.-h.</col_3><col_4><body>Sec.-h.</col_4></row_11>
 <row_12><col_0><row_header>Overall</col_0><col_1><body>72</col_1><col_2><body>73</col_2><col_3><body>78</col_3><col_4><body>77</col_4></row_12>
 </table>
+<subtitle-level-1><location><page_7><loc_9><loc_58><loc_21><loc_60></location>Learning Curve</subtitle-level-1>
+<paragraph><location><page_7><loc_9><loc_33><loc_48><loc_58></location>One of the fundamental questions related to any dataset is if it is "large enough". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.</paragraph>
+<subtitle-level-1><location><page_7><loc_9><loc_30><loc_27><loc_32></location>Impact of Class Labels</subtitle-level-1>
+<paragraph><location><page_7><loc_9><loc_11><loc_48><loc_30></location>The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption → Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of</paragraph>
 <table>
-<location><page_7><loc_59><loc_62><loc_85><loc_81></location>
+<location><page_7><loc_58><loc_61><loc_85><loc_81></location>
 <row_0><col_0><body>Class-count</col_0><col_1><col_header>11</col_1><col_2><col_header>11</col_2><col_3><col_header>5</col_3><col_4><col_header>5</col_4></row_0>
 <row_1><col_0><body>Split</col_0><col_1><col_header>Doc</col_1><col_2><col_header>Page</col_2><col_3><col_header>Doc</col_3><col_4><col_header>Page</col_4></row_1>
 <row_2><col_0><row_header>Caption</col_0><col_1><body>68</col_1><col_2><body>83</col_2><col_3><body></col_3><col_4><body></col_4></row_2>
@@ -170,10 +174,6 @@
 <row_12><col_0><row_header>Title</col_0><col_1><body>77</col_1><col_2><body>81</col_2><col_3><body></col_3><col_4><body></col_4></row_12>
 <row_13><col_0><row_header>All</col_0><col_1><body>72</col_1><col_2><body>84</col_2><col_3><body>78</col_3><col_4><body>87</col_4></row_13>
 </table>
-<subtitle-level-1><location><page_7><loc_9><loc_58><loc_21><loc_60></location>Learning Curve</subtitle-level-1>
-<paragraph><location><page_7><loc_9><loc_33><loc_48><loc_58></location>One of the fundamental questions related to any dataset is if it is "large enough". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.</paragraph>
-<subtitle-level-1><location><page_7><loc_9><loc_30><loc_27><loc_32></location>Impact of Class Labels</subtitle-level-1>
-<paragraph><location><page_7><loc_9><loc_11><loc_48><loc_30></location>The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption → Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of</paragraph>
 <paragraph><location><page_7><loc_52><loc_47><loc_91><loc_58></location>lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.</paragraph>
 <subtitle-level-1><location><page_7><loc_52><loc_44><loc_90><loc_46></location>Impact of Document Split in Train and Test Set</subtitle-level-1>
 <paragraph><location><page_7><loc_52><loc_25><loc_91><loc_44></location>Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains ˜ 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided.</paragraph>
@@ -181,23 +181,23 @@
 <paragraph><location><page_7><loc_52><loc_11><loc_91><loc_21></location>Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,</paragraph>
 <caption><location><page_8><loc_9><loc_81><loc_48><loc_89></location>Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.</caption>
 <table>
-<location><page_8><loc_13><loc_57><loc_44><loc_78></location>
+<location><page_8><loc_12><loc_57><loc_45><loc_78></location>
 <caption>Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.</caption>
 <row_0><col_0><body></col_0><col_1><body></col_1><col_2><col_header>Testing on</col_2><col_3><col_header>Testing on</col_3><col_4><col_header>Testing on</col_4></row_0>
 <row_1><col_0><col_header>Training on</col_0><col_1><col_header>labels</col_1><col_2><col_header>PLN</col_2><col_3><col_header>DB</col_3><col_4><col_header>DLN</col_4></row_1>
-<row_2><col_0><body></col_0><col_1><body>Figure</col_1><col_2><body>96</col_2><col_3><body>43</col_3><col_4><body>23</col_4></row_2>
-<row_3><col_0><body></col_0><col_1><body>Sec-header</col_1><col_2><body>87</col_2><col_3><body>-</col_3><col_4><body>32</col_4></row_3>
-<row_4><col_0><body>PubLayNet (PLN)</col_0><col_1><body>Table</col_1><col_2><body>95</col_2><col_3><body>24</col_3><col_4><body>49</col_4></row_4>
-<row_5><col_0><body></col_0><col_1><body>Text</col_1><col_2><body>96</col_2><col_3><body>-</col_3><col_4><body>42</col_4></row_5>
-<row_6><col_0><body></col_0><col_1><body>total</col_1><col_2><body>93</col_2><col_3><body>34</col_3><col_4><body>30</col_4></row_6>
-<row_7><col_0><body></col_0><col_1><body>Figure</col_1><col_2><body>77</col_2><col_3><body>71</col_3><col_4><body>31</col_4></row_7>
-<row_8><col_0><body>DocBank (DB)</col_0><col_1><body>Table</col_1><col_2><body>19</col_2><col_3><body>65</col_3><col_4><body>22</col_4></row_8>
-<row_9><col_0><body></col_0><col_1><body>total</col_1><col_2><body>48</col_2><col_3><body>68</col_3><col_4><body>27</col_4></row_9>
-<row_10><col_0><body></col_0><col_1><body>Figure</col_1><col_2><body>67</col_2><col_3><body>51</col_3><col_4><body>72</col_4></row_10>
-<row_11><col_0><body></col_0><col_1><body>Sec-header</col_1><col_2><body>53</col_2><col_3><body>-</col_3><col_4><body>68</col_4></row_11>
-<row_12><col_0><body>DocLayNet (DLN)</col_0><col_1><body>Table</col_1><col_2><body>87</col_2><col_3><body>43</col_3><col_4><body>82</col_4></row_12>
-<row_13><col_0><body></col_0><col_1><body>Text</col_1><col_2><body>77</col_2><col_3><body>-</col_3><col_4><body>84</col_4></row_13>
-<row_14><col_0><body></col_0><col_1><body>total</col_1><col_2><body>59</col_2><col_3><body>47</col_3><col_4><body>78</col_4></row_14>
+<row_2><col_0><row_header>PubLayNet (PLN)</col_0><col_1><row_header>Figure</col_1><col_2><body>96</col_2><col_3><body>43</col_3><col_4><body>23</col_4></row_2>
+<row_3><col_0><row_header>PubLayNet (PLN)</col_0><col_1><row_header>Sec-header</col_1><col_2><body>87</col_2><col_3><body>-</col_3><col_4><body>32</col_4></row_3>
+<row_4><col_0><row_header>PubLayNet (PLN)</col_0><col_1><row_header>Table</col_1><col_2><body>95</col_2><col_3><body>24</col_3><col_4><body>49</col_4></row_4>
+<row_5><col_0><row_header>PubLayNet (PLN)</col_0><col_1><row_header>Text</col_1><col_2><body>96</col_2><col_3><body>-</col_3><col_4><body>42</col_4></row_5>
+<row_6><col_0><row_header>PubLayNet (PLN)</col_0><col_1><row_header>total</col_1><col_2><body>93</col_2><col_3><body>34</col_3><col_4><body>30</col_4></row_6>
+<row_7><col_0><row_header>DocBank (DB)</col_0><col_1><row_header>Figure</col_1><col_2><body>77</col_2><col_3><body>71</col_3><col_4><body>31</col_4></row_7>
+<row_8><col_0><row_header>DocBank (DB)</col_0><col_1><row_header>Table</col_1><col_2><body>19</col_2><col_3><body>65</col_3><col_4><body>22</col_4></row_8>
+<row_9><col_0><row_header>DocBank (DB)</col_0><col_1><row_header>total</col_1><col_2><body>48</col_2><col_3><body>68</col_3><col_4><body>27</col_4></row_9>
+<row_10><col_0><row_header>DocLayNet (DLN)</col_0><col_1><row_header>Figure</col_1><col_2><body>67</col_2><col_3><body>51</col_3><col_4><body>72</col_4></row_10>
+<row_11><col_0><row_header>DocLayNet (DLN)</col_0><col_1><row_header>Sec-header</col_1><col_2><body>53</col_2><col_3><body>-</col_3><col_4><body>68</col_4></row_11>
+<row_12><col_0><row_header>DocLayNet (DLN)</col_0><col_1><row_header>Table</col_1><col_2><body>87</col_2><col_3><body>43</col_3><col_4><body>82</col_4></row_12>
+<row_13><col_0><row_header>DocLayNet (DLN)</col_0><col_1><row_header>Text</col_1><col_2><body>77</col_2><col_3><body>-</col_3><col_4><body>84</col_4></row_13>
+<row_14><col_0><row_header>DocLayNet (DLN)</col_0><col_1><row_header>total</col_1><col_2><body>59</col_2><col_3><body>47</col_3><col_4><body>78</col_4></row_14>
 </table>
 <paragraph><location><page_8><loc_9><loc_44><loc_48><loc_51></location>Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .</paragraph>
 <paragraph><location><page_8><loc_9><loc_26><loc_48><loc_44></location>For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts.</paragraph>
diff --git a/tests/data/groundtruth/docling_v1/2206.01062.json b/tests/data/groundtruth/docling_v1/2206.01062.json
index 98bfe6ec..efe041e3 100644
--- a/tests/data/groundtruth/docling_v1/2206.01062.json
+++ b/tests/data/groundtruth/docling_v1/2206.01062.json
@@ -1 +1 @@
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Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [336.6930236816406, 553.3746948242188, 439.457275390625, 599.942626953125], "page": 1, "span": [0, 68], "__ref_s3_data": null}], "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79803466796875, 533.9879760742188, 111.94354248046875, 544.297119140625], "page": 1, "span": [0, 8], "__ref_s3_data": null}], "text": "ABSTRACT", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.46699905395508, 257.7068176269531, 295.5601806640625, 529.095458984375], "page": 1, "span": [0, 1595], "__ref_s3_data": null}], "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 230.69398498535156, 134.81988525390625, 241.00308227539062], "page": 1, "span": [0, 12], "__ref_s3_data": null}], "text": "CCS CONCEPTS", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.79798889160156, 195.4988555908203, 297.8529357910156, 225.91700744628906], "page": 1, "span": [0, 170], "__ref_s3_data": null}], "text": "\u00b7 Information systems \u2192 Document structure ; \u00b7 Applied computing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 119.2081069946289, 295.11798095703125, 157.60162353515625], "page": 1, "span": [0, 397], "__ref_s3_data": null}], "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 110.43414306640625, 197.8627471923828, 116.91976928710938], "page": 1, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.31700134277344, 101.67411041259766, 186.74652099609375, 108.18763732910156], "page": 1, "span": [0, 45], "__ref_s3_data": null}], "text": "\u00a9 2022 Copyright held by the owner/author(s).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.55400085449219, 93.70310974121094, 157.03125, 100.21663665771484], "page": 1, "span": [0, 33], "__ref_s3_data": null}], "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 85.73310852050781, 166.94093322753906, 92.24663543701172], "page": 1, "span": [0, 39], "__ref_s3_data": null}], "text": "https://doi.org/10.1145/3534678.3539043", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 232.48475646972656, 559.8057861328125, 251.91700744628906], "page": 1, "span": [0, 84], "__ref_s3_data": null}], "text": "Figure 1: Four examples of complex page layouts across different document categories", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [317.9549865722656, 189.22499084472656, 379.82049560546875, 199.53408813476562], "page": 1, "span": [0, 8], "__ref_s3_data": null}], "text": "KEYWORDS", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.9549865722656, 164.9988250732422, 559.1859741210938, 184.3324432373047], "page": 1, "span": [0, 90], "__ref_s3_data": null}], "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.65997314453125, 144.41390991210938, 404.6536560058594, 151.94566345214844], "page": 1, "span": [0, 21], "__ref_s3_data": null}], "text": "ACM Reference Format:", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.9549865722656, 84.62297058105469, 559.5494995117188, 141.88003540039062], "page": 1, "span": [0, 374], "__ref_s3_data": null}], "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 558.202880859375, 731.6909790039062], "page": 2, "span": [0, 130], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.79800033569336, 695.8309936523438, 156.52899169921875, 706.14013671875], "page": 2, "span": [0, 14], "__ref_s3_data": null}], "text": "1 INTRODUCTION", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.52899932861328, 563.0528564453125, 303.0169677734375, 681.0164794921875], "page": 2, "span": [0, 702], "__ref_s3_data": null}], "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.52899932861328, 289.0808410644531, 295.5641174316406, 560.4684448242188], "page": 2, "span": [0, 1580], "__ref_s3_data": null}], "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.59199905395508, 212.36782836914062, 295.56396484375, 286.4964599609375], "page": 2, "span": [0, 462], "__ref_s3_data": null}], "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.70800018310547, 177.12582397460938, 295.5616455078125, 207.41844177246094], "page": 2, "span": [0, 149], "__ref_s3_data": null}], "text": "- (1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 155.20883178710938, 294.2625427246094, 174.54144287109375], "page": 2, "span": [0, 109], "__ref_s3_data": null}], "text": "- (2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 122.33183288574219, 294.6838073730469, 152.62445068359375], "page": 2, "span": [0, 180], "__ref_s3_data": null}], "text": "- (3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 100.41383361816406, 295.56439208984375, 119.7474365234375], "page": 2, "span": [0, 115], "__ref_s3_data": null}], "text": "- (4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.672000885009766, 83.2601089477539, 216.02749633789062, 89.77363586425781], "page": 2, "span": [0, 60], "__ref_s3_data": null}], "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [342.0950012207031, 685.3028564453125, 558.4320068359375, 704.636474609375], "page": 2, "span": [0, 86], "__ref_s3_data": null}], "text": "This enables experimentation with annotation uncertainty and quality control analysis.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [328.8650207519531, 630.5088500976562, 559.7210083007812, 682.718505859375], "page": 2, "span": [0, 280], "__ref_s3_data": null}], "text": "- (5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.62298583984375, 571.8138427734375, 559.1903076171875, 624.0244750976562], "page": 2, "span": [0, 297], "__ref_s3_data": null}], "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.7309875488281, 484.142822265625, 559.5819702148438, 569.2294311523438], "page": 2, "span": [0, 506], "__ref_s3_data": null}], "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 460.4820251464844, 421.7441101074219, 470.7911071777344], "page": 2, "span": [0, 14], "__ref_s3_data": null}], "text": "2 RELATED WORK", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.5249938964844, 327.7038269042969, 559.7161254882812, 445.6674499511719], "page": 2, "span": [0, 655], "__ref_s3_data": null}], "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 240.03182983398438, 559.1864624023438, 325.1194763183594], "page": 2, "span": [0, 500], "__ref_s3_data": null}], "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 216.37100219726562, 477.4568786621094, 226.6800994873047], "page": 2, "span": [0, 23], "__ref_s3_data": null}], "text": "3 THE DOCLAYNET DATASET", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.9549865722656, 116.46983337402344, 559.7131958007812, 201.5564422607422], "page": 2, "span": [0, 522], "__ref_s3_data": null}], "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 83.59282684326172, 558.2041015625, 113.88543701171875], "page": 2, "span": [0, 186], "__ref_s3_data": null}], "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 347.0172424316406, 731.6909790039062], "page": 3, "span": [0, 71], "__ref_s3_data": null}], "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [365.75701904296875, 723.4239501953125, 558.2028198242188, 731.6909790039062], "page": 3, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.79800033569336, 536.4527587890625, 294.0437316894531, 555.885009765625], "page": 3, "span": [0, 69], "__ref_s3_data": null}], "text": "Figure 2: Distribution of DocLayNet pages across document categories.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"prov": [{"bbox": [53.79800033569336, 425.1098327636719, 294.2738342285156, 510.19647216796875], "page": 3, "span": [0, 513], "__ref_s3_data": null}], "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\".", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.57400131225586, 282.6438293457031, 295.5604553222656, 422.52545166015625], "page": 3, "span": [0, 810], "__ref_s3_data": null}], "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.46699905395508, 184.01382446289062, 295.5615539550781, 280.0594482421875], "page": 3, "span": [0, 535], "__ref_s3_data": null}], "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 107.30182647705078, 295.56396484375, 181.429443359375], "page": 3, "span": [0, 413], "__ref_s3_data": null}], "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 83.83010864257812, 195.78997802734375, 90.34363555908203], "page": 3, "span": [0, 51], "__ref_s3_data": null}], "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [317.62298583984375, 630.5088500976562, 559.1918334960938, 704.636474609375], "page": 3, "span": [0, 435], "__ref_s3_data": null}], "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 520.9197998046875, 558.4381103515625, 627.9244384765625], "page": 3, "span": [0, 645], "__ref_s3_data": null}], "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [316.9419860839844, 203.11082458496094, 559.7215576171875, 518.33544921875], "page": 3, "span": [0, 1854], "__ref_s3_data": null}], "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 174.8409881591797, 470.2132568359375, 185.15008544921875], "page": 3, "span": [0, 21], "__ref_s3_data": null}], "text": "4 ANNOTATION CAMPAIGN", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.6860046386719, 85.8978271484375, 559.7138061523438, 160.0264434814453], "page": 3, "span": [0, 457], "__ref_s3_data": null}], "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 558.202880859375, 731.6909790039062], "page": 4, "span": [0, 130], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.50199890136719, 676.65380859375, 558.4896850585938, 707.0450439453125], "page": 4, "span": [0, 348], "__ref_s3_data": null}], "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [53.79800033569336, 185.68075561523438, 295.64874267578125, 237.99000549316406], "page": 4, "span": [0, 281], "__ref_s3_data": null}], "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"prov": [{"bbox": [53.46699905395508, 116.45683288574219, 294.0474548339844, 157.7084503173828], "page": 4, "span": [0, 231], "__ref_s3_data": null}], "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 83.57982635498047, 295.5584411621094, 113.989013671875], "page": 4, "span": [0, 193], "__ref_s3_data": null}], "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 416.7518310546875, 559.1853637695312, 479.92047119140625], "page": 4, "span": [0, 376], "__ref_s3_data": null}], "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 285.2448425292969, 559.7130737304688, 414.1674499511719], "page": 4, "span": [0, 746], "__ref_s3_data": null}], "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.62298583984375, 98.9438247680664, 559.7176513671875, 282.7770080566406], "page": 4, "span": [0, 1159], "__ref_s3_data": null}], "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 83.13311004638672, 369.2456970214844, 89.64663696289062], "page": 4, "span": [0, 24], "__ref_s3_data": null}], "text": "$^{3}$https://arxiv.org/", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 347.0172424316406, 731.6909790039062], "page": 5, "span": [0, 71], "__ref_s3_data": null}], "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [365.75701904296875, 723.4239501953125, 558.2028198242188, 731.6909790039062], "page": 5, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.79800033569336, 685.2938842773438, 294.04541015625, 704.636474609375], "page": 5, "span": [0, 135], "__ref_s3_data": null}], "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 542.8378295898438, 295.5592346191406, 682.7184448242188], "page": 5, "span": [0, 812], "__ref_s3_data": null}], "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 455.16583251953125, 295.56005859375, 540.2534790039062], "page": 5, "span": [0, 465], "__ref_s3_data": null}], "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.70800018310547, 402.22686767578125, 294.04620361328125, 443.4874572753906], "page": 5, "span": [0, 202], "__ref_s3_data": null}], "text": "- (1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70799255371094, 358.39984130859375, 295.563720703125, 399.6514892578125], "page": 5, "span": [0, 208], "__ref_s3_data": null}], "text": "- (2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 336.4728698730469, 294.0472412109375, 355.81549072265625], "page": 5, "span": [0, 82], "__ref_s3_data": null}], "text": "- (3) For every Caption , there must be exactly one corresponding Picture or Table .", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 314.5648193359375, 294.0459899902344, 333.8984680175781], "page": 5, "span": [0, 70], "__ref_s3_data": null}], "text": "- (4) Connected sub-pictures are grouped together in one Picture object.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 303.59686279296875, 264.5057067871094, 311.98046875], "page": 5, "span": [0, 53], "__ref_s3_data": null}], "text": "- (5) Formula numbers are included in a Formula object.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.7080078125, 270.72882080078125, 294.0461730957031, 301.021484375], "page": 5, "span": [0, 160], "__ref_s3_data": null}], "text": "- (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.52899932861328, 217.798828125, 295.5625305175781, 259.0494689941406], "page": 5, "span": [0, 221], "__ref_s3_data": null}], "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 86.29182434082031, 295.562255859375, 215.3310089111328], "page": 5, "span": [0, 792], "__ref_s3_data": null}], "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 288.11480712890625, 559.8057861328125, 318.5060119628906], "page": 5, "span": [0, 173], "__ref_s3_data": null}], "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [400.12841796875, 331.43994140625, 476.331787109375, 333.5567321777344], "page": 5, "span": [0, 64], "__ref_s3_data": null}], "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.62298583984375, 247.1688232421875, 558.204345703125, 266.5024719238281], "page": 5, "span": [0, 123], "__ref_s3_data": null}], "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.62298583984375, 82.78482818603516, 559.7149047851562, 244.7010040283203], "page": 5, "span": [0, 987], "__ref_s3_data": null}], "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 558.202880859375, 731.6909790039062], "page": 6, "span": [0, 130], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.50199890136719, 608.98291015625, 295.64874267578125, 705.1270751953125], "page": 6, "span": [0, 489], "__ref_s3_data": null}], "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [53.52899932861328, 215.43682861328125, 295.5561218261719, 421.07244873046875], "page": 6, "span": [0, 1252], "__ref_s3_data": null}], "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 193.5609893798828, 147.4853515625, 203.87008666992188], "page": 6, "span": [0, 13], "__ref_s3_data": null}], "text": "5 EXPERIMENTS", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.48400115966797, 82.7008285522461, 295.4281005859375, 178.74644470214844], "page": 6, "span": [0, 584], "__ref_s3_data": null}], "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 449.7158203125, 559.8057861328125, 512.9840087890625], "page": 6, "span": [0, 329], "__ref_s3_data": null}], "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [317.9549865722656, 388.6548156738281, 558.2041625976562, 407.98846435546875], "page": 6, "span": [0, 102], "__ref_s3_data": null}], "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.6409912109375, 311.9428405761719, 558.4364013671875, 386.0704650878906], "page": 6, "span": [0, 397], "__ref_s3_data": null}], "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 284.8690185546875, 466.8532409667969, 295.1781005859375], "page": 6, "span": [0, 30], "__ref_s3_data": null}], "text": "Baselines for Object Detection", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.7489929199219, 85.2998275756836, 558.4308471679688, 279.9754638671875], "page": 6, "span": [0, 1146], "__ref_s3_data": null}], "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 347.0172424316406, 731.6909790039062], "page": 7, "span": [0, 71], "__ref_s3_data": null}], "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [365.75701904296875, 723.4239501953125, 558.2028198242188, 731.6909790039062], "page": 7, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.50199890136719, 663.77685546875, 295.6486511230469, 705.1270751953125], "page": 7, "span": [0, 205], "__ref_s3_data": null}], "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.65899658203125, 663.7767944335938, 559.8068237304688, 705.1270141601562], "page": 7, "span": [0, 189], "__ref_s3_data": null}], "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"name": "Table", "type": "table", "$ref": "#/tables/3"}, {"prov": [{"bbox": [53.79800033569336, 462.1210021972656, 131.05624389648438, 472.4300842285156], "page": 7, "span": [0, 14], "__ref_s3_data": null}], "text": "Learning Curve", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [52.78499984741211, 262.55181884765625, 295.558349609375, 457.22845458984375], "page": 7, "span": [0, 1157], "__ref_s3_data": null}], "text": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 239.1809844970703, 164.3289794921875, 249.49008178710938], "page": 7, "span": [0, 22], "__ref_s3_data": null}], "text": "Impact of Class Labels", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.46699905395508, 83.44783020019531, 295.5567932128906, 234.2884521484375], "page": 7, "span": [0, 910], "__ref_s3_data": null}], "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.6860046386719, 375.50982666015625, 559.5849609375, 460.5964660644531], "page": 7, "span": [0, 469], "__ref_s3_data": null}], "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549560546875, 352.2960205078125, 549.860595703125, 362.6051025390625], "page": 7, "span": [0, 46], "__ref_s3_data": null}], "text": "Impact of Document Split in Train and Test Set", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.62298583984375, 196.5628204345703, 559.7138061523438, 347.4034729003906], "page": 7, "span": [0, 852], "__ref_s3_data": null}], "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 173.34898376464844, 418.5477600097656, 183.6580810546875], "page": 7, "span": [0, 18], "__ref_s3_data": null}], "text": "Dataset Comparison", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.6860046386719, 83.35986328125, 559.1881713867188, 168.45645141601562], "page": 7, "span": [0, 521], "__ref_s3_data": null}], "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 558.202880859375, 731.6909790039062], "page": 8, "span": [0, 130], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.50199890136719, 641.85888671875, 295.648681640625, 705.1270751953125], "page": 8, "span": [0, 298], "__ref_s3_data": null}], "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/4"}, {"prov": [{"bbox": [53.79800033569336, 348.85986328125, 294.047119140625, 401.0794677734375], "page": 8, "span": [0, 295], "__ref_s3_data": null}], "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.46699905395508, 206.40382385253906, 295.55908203125, 346.28546142578125], "page": 8, "span": [0, 793], "__ref_s3_data": null}], "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 176.62998962402344, 156.00534057617188, 186.9390869140625], "page": 8, "span": [0, 19], "__ref_s3_data": null}], "text": "Example Predictions", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.52899932861328, 86.64982604980469, 295.5584411621094, 171.7364501953125], "page": 8, "span": [0, 481], "__ref_s3_data": null}], "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.95501708984375, 695.8309936523438, 405.7296142578125, 706.14013671875], "page": 8, "span": [0, 12], "__ref_s3_data": null}], "text": "6 CONCLUSION", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.9549865722656, 605.850830078125, 559.7137451171875, 690.9384765625], "page": 8, "span": [0, 507], "__ref_s3_data": null}], "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.6860046386719, 507.2208251953125, 559.717041015625, 603.2664794921875], "page": 8, "span": [0, 573], "__ref_s3_data": null}], "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.62298583984375, 474.3438415527344, 558.4346923828125, 504.636474609375], "page": 8, "span": [0, 188], "__ref_s3_data": null}], "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 446.5990295410156, 387.3695983886719, 456.9081115722656], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "REFERENCES", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [321.197998046875, 420.8371276855469, 558.2009887695312, 443.29766845703125], "page": 8, "span": [0, 191], "__ref_s3_data": null}], "text": "- [1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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IEEE Computer Society, Oct 2017.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 86.09910583496094, 558.9715576171875, 116.52364349365234], "page": 8, "span": [0, 305], "__ref_s3_data": null}], "text": "- [13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 347.0172424316406, 731.6909790039062], "page": 9, "span": [0, 71], "__ref_s3_data": null}], "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [365.75701904296875, 723.4239501953125, 558.2028198242188, 731.6909790039062], "page": 9, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [62.323875427246094, 343.73516845703125, 318.5047302246094, 349.7145690917969], "page": 9, "span": [0, 89], "__ref_s3_data": null}], "text": "Text Caption List-Item Formula Table Section-Header Picture Page-Header Page-Footer Title", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [53.79800033569336, 286.16876220703125, 559.807861328125, 327.51800537109375], "page": 9, "span": [0, 386], "__ref_s3_data": null}], "text": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [69.23400115966797, 242.4801025390625, 295.22406005859375, 264.93365478515625], "page": 9, "span": [0, 195], "__ref_s3_data": null}], "text": "Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ultralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow export, opencv dnn support, October 2021.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 218.56314086914062, 295.12176513671875, 241.02362060546875], "page": 9, "span": [0, 190], "__ref_s3_data": null}], "text": "- [14] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. CoRR , abs/2005.12872, 2020.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.79800033569336, 202.62213134765625, 294.042236328125, 217.1136474609375], "page": 9, "span": [0, 132], "__ref_s3_data": null}], "text": "- [15] Mingxing Tan, Ruoming Pang, and Quoc V. Le. Efficientdet: Scalable and efficient object detection. CoRR , abs/1911.09070, 2019.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.798004150390625, 178.71910095214844, 295.2226257324219, 201.17263793945312], "page": 9, "span": [0, 219], "__ref_s3_data": null}], "text": "- [16] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll\u00e1r, and C. Lawrence Zitnick. Microsoft COCO: common objects in context, 2014.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.79800033569336, 162.77911376953125, 295.1200866699219, 177.26263427734375], "page": 9, "span": [0, 100], "__ref_s3_data": null}], "text": "- [17] Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detectron2, 2019.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.79800033569336, 122.92810821533203, 294.80889892578125, 161.3226318359375], "page": 9, "span": [0, 339], "__ref_s3_data": null}], "text": "- [18] Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak, Viktor Kuropiatnyk, Ahmed Nassar, Andre Carvalho, Michele Dolfi, Christoph Auer, Kasper Dinkla, and Peter W. J. Staar. Robust pdf document conversion using recurrent neural networks. In Proceedings of the 35th Conference on Artificial Intelligence , AAAI, pages 1513715145, feb 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.797996520996094, 83.07810974121094, 295.22174072265625, 121.47162628173828], "page": 9, "span": [0, 336], "__ref_s3_data": null}], "text": "- [19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 1192-1200, New York, USA, 2020. Association for Computing Machinery.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 250.45010375976562, 559.0263671875, 264.9336242675781], "page": 9, "span": [0, 153], "__ref_s3_data": null}], "text": "- [20] Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. Vtlayout: Fusion of visual and text features for document layout analysis, 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 226.54010009765625, 558.9714965820312, 248.99362182617188], "page": 9, "span": [0, 188], "__ref_s3_data": null}], "text": "- [21] Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Fei Wu. Vsr: A unified framework for document layout analysis combining vision, semantics and relations, 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 194.65213012695312, 559.275390625, 225.08364868164062], "page": 9, "span": [0, 290], "__ref_s3_data": null}], "text": "- [22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 774-782. ACM, 2018.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 178.71212768554688, 559.3782958984375, 193.20263671875], "page": 9, "span": [0, 138], "__ref_s3_data": null}], "text": "- [23] Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data , 6(1):60, 2019.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}], "figures": [{"prov": [{"bbox": [323.40838623046875, 266.14923095703125, 553.295166015625, 541.6513671875], "page": 1, "span": [0, 84], "__ref_s3_data": null}], "text": "Figure 1: Four examples of complex page layouts across different document categories", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [88.32998657226562, 571.4320068359375, 263.70513916015625, 699.113525390625], "page": 3, "span": [0, 69], "__ref_s3_data": null}], "text": "Figure 2: Distribution of DocLayNet pages across document categories.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [53.05910873413086, 251.1358642578125, 295.8505554199219, 481.2087097167969], "page": 4, "span": [0, 281], "__ref_s3_data": null}], "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [315.960205078125, 332.31915283203125, 559.396484375, 706.6611938476562], "page": 5, "span": [0, 173], "__ref_s3_data": null}], "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [323.4842224121094, 531.9901733398438, 553.5410766601562, 702.1138305664062], "page": 6, "span": [0, 329], "__ref_s3_data": null}], "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [52.964385986328125, 349.8664245605469, 556.9307861328125, 707.2642822265625], "page": 9, "span": [0, 89], "__ref_s3_data": null}], "text": "Text Caption List-Item Formula Table Section-Header Picture Page-Header Page-Footer Title", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [104.82499694824219, 500.1388244628906, 507.1784973144531, 651.7764892578125], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. 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Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [336.6930236816406, 553.3746948242188, 439.457275390625, 599.942626953125], "page": 1, "span": [0, 68], "__ref_s3_data": null}], "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79803466796875, 533.9879760742188, 111.94354248046875, 544.297119140625], "page": 1, "span": [0, 8], "__ref_s3_data": null}], "text": "ABSTRACT", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.46699905395508, 257.7068176269531, 295.5601806640625, 529.095458984375], "page": 1, "span": [0, 1595], "__ref_s3_data": null}], "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 230.69398498535156, 134.81988525390625, 241.00308227539062], "page": 1, "span": [0, 12], "__ref_s3_data": null}], "text": "CCS CONCEPTS", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.79798889160156, 195.4988555908203, 297.8529357910156, 225.91700744628906], "page": 1, "span": [0, 170], "__ref_s3_data": null}], "text": "\u00b7 Information systems \u2192 Document structure ; \u00b7 Applied computing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 119.2081069946289, 295.11798095703125, 157.60162353515625], "page": 1, "span": [0, 397], "__ref_s3_data": null}], "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 110.43414306640625, 197.8627471923828, 116.91976928710938], "page": 1, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.31700134277344, 101.67411041259766, 186.74652099609375, 108.18763732910156], "page": 1, "span": [0, 45], "__ref_s3_data": null}], "text": "\u00a9 2022 Copyright held by the owner/author(s).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.55400085449219, 93.70310974121094, 157.03125, 100.21663665771484], "page": 1, "span": [0, 33], "__ref_s3_data": null}], "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 85.73310852050781, 166.94093322753906, 92.24663543701172], "page": 1, "span": [0, 39], "__ref_s3_data": null}], "text": "https://doi.org/10.1145/3534678.3539043", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 232.48475646972656, 559.8057861328125, 251.91700744628906], "page": 1, "span": [0, 84], "__ref_s3_data": null}], "text": "Figure 1: Four examples of complex page layouts across different document categories", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [317.9549865722656, 189.22499084472656, 379.82049560546875, 199.53408813476562], "page": 1, "span": [0, 8], "__ref_s3_data": null}], "text": "KEYWORDS", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.9549865722656, 164.9988250732422, 559.1859741210938, 184.3324432373047], "page": 1, "span": [0, 90], "__ref_s3_data": null}], "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.65997314453125, 144.41390991210938, 404.6536560058594, 151.94566345214844], "page": 1, "span": [0, 21], "__ref_s3_data": null}], "text": "ACM Reference Format:", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.9549865722656, 84.62297058105469, 559.5494995117188, 141.88003540039062], "page": 1, "span": [0, 374], "__ref_s3_data": null}], "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 558.202880859375, 731.6909790039062], "page": 2, "span": [0, 130], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.79800033569336, 695.8309936523438, 156.52899169921875, 706.14013671875], "page": 2, "span": [0, 14], "__ref_s3_data": null}], "text": "1 INTRODUCTION", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.52899932861328, 563.0528564453125, 303.0169677734375, 681.0164794921875], "page": 2, "span": [0, 702], "__ref_s3_data": null}], "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.52899932861328, 289.0808410644531, 295.5641174316406, 560.4684448242188], "page": 2, "span": [0, 1580], "__ref_s3_data": null}], "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.59199905395508, 212.36782836914062, 295.56396484375, 286.4964599609375], "page": 2, "span": [0, 462], "__ref_s3_data": null}], "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.70800018310547, 177.12582397460938, 295.5616455078125, 207.41844177246094], "page": 2, "span": [0, 149], "__ref_s3_data": null}], "text": "- (1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 155.20883178710938, 294.2625427246094, 174.54144287109375], "page": 2, "span": [0, 109], "__ref_s3_data": null}], "text": "- (2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 122.33183288574219, 294.6838073730469, 152.62445068359375], "page": 2, "span": [0, 180], "__ref_s3_data": null}], "text": "- (3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 100.41383361816406, 295.56439208984375, 119.7474365234375], "page": 2, "span": [0, 115], "__ref_s3_data": null}], "text": "- (4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.672000885009766, 83.2601089477539, 216.02749633789062, 89.77363586425781], "page": 2, "span": [0, 60], "__ref_s3_data": null}], "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [342.0950012207031, 685.3028564453125, 558.4320068359375, 704.636474609375], "page": 2, "span": [0, 86], "__ref_s3_data": null}], "text": "This enables experimentation with annotation uncertainty and quality control analysis.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [328.8650207519531, 630.5088500976562, 559.7210083007812, 682.718505859375], "page": 2, "span": [0, 280], "__ref_s3_data": null}], "text": "- (5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.62298583984375, 571.8138427734375, 559.1903076171875, 624.0244750976562], "page": 2, "span": [0, 297], "__ref_s3_data": null}], "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.7309875488281, 484.142822265625, 559.5819702148438, 569.2294311523438], "page": 2, "span": [0, 506], "__ref_s3_data": null}], "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 460.4820251464844, 421.7441101074219, 470.7911071777344], "page": 2, "span": [0, 14], "__ref_s3_data": null}], "text": "2 RELATED WORK", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.5249938964844, 327.7038269042969, 559.7161254882812, 445.6674499511719], "page": 2, "span": [0, 655], "__ref_s3_data": null}], "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 240.03182983398438, 559.1864624023438, 325.1194763183594], "page": 2, "span": [0, 500], "__ref_s3_data": null}], "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 216.37100219726562, 477.4568786621094, 226.6800994873047], "page": 2, "span": [0, 23], "__ref_s3_data": null}], "text": "3 THE DOCLAYNET DATASET", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.9549865722656, 116.46983337402344, 559.7131958007812, 201.5564422607422], "page": 2, "span": [0, 522], "__ref_s3_data": null}], "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 83.59282684326172, 558.2041015625, 113.88543701171875], "page": 2, "span": [0, 186], "__ref_s3_data": null}], "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 347.0172424316406, 731.6909790039062], "page": 3, "span": [0, 71], "__ref_s3_data": null}], "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [365.75701904296875, 723.4239501953125, 558.2028198242188, 731.6909790039062], "page": 3, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.79800033569336, 536.4527587890625, 294.0437316894531, 555.885009765625], "page": 3, "span": [0, 69], "__ref_s3_data": null}], "text": "Figure 2: Distribution of DocLayNet pages across document categories.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"prov": [{"bbox": [53.79800033569336, 425.1098327636719, 294.2738342285156, 510.19647216796875], "page": 3, "span": [0, 513], "__ref_s3_data": null}], "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\".", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.57400131225586, 282.6438293457031, 295.5604553222656, 422.52545166015625], "page": 3, "span": [0, 810], "__ref_s3_data": null}], "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.46699905395508, 184.01382446289062, 295.5615539550781, 280.0594482421875], "page": 3, "span": [0, 535], "__ref_s3_data": null}], "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 107.30182647705078, 295.56396484375, 181.429443359375], "page": 3, "span": [0, 413], "__ref_s3_data": null}], "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 83.83010864257812, 195.78997802734375, 90.34363555908203], "page": 3, "span": [0, 51], "__ref_s3_data": null}], "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [317.62298583984375, 630.5088500976562, 559.1918334960938, 704.636474609375], "page": 3, "span": [0, 435], "__ref_s3_data": null}], "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 520.9197998046875, 558.4381103515625, 627.9244384765625], "page": 3, "span": [0, 645], "__ref_s3_data": null}], "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [316.9419860839844, 203.11082458496094, 559.7215576171875, 518.33544921875], "page": 3, "span": [0, 1854], "__ref_s3_data": null}], "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 174.8409881591797, 470.2132568359375, 185.15008544921875], "page": 3, "span": [0, 21], "__ref_s3_data": null}], "text": "4 ANNOTATION CAMPAIGN", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.6860046386719, 85.8978271484375, 559.7138061523438, 160.0264434814453], "page": 3, "span": [0, 457], "__ref_s3_data": null}], "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 558.202880859375, 731.6909790039062], "page": 4, "span": [0, 130], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.50199890136719, 676.65380859375, 558.4896850585938, 707.0450439453125], "page": 4, "span": [0, 348], "__ref_s3_data": null}], "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [53.79800033569336, 185.68075561523438, 295.64874267578125, 237.99000549316406], "page": 4, "span": [0, 281], "__ref_s3_data": null}], "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"prov": [{"bbox": [53.46699905395508, 116.45683288574219, 294.0474548339844, 157.7084503173828], "page": 4, "span": [0, 231], "__ref_s3_data": null}], "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 83.57982635498047, 295.5584411621094, 113.989013671875], "page": 4, "span": [0, 193], "__ref_s3_data": null}], "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 416.7518310546875, 559.1853637695312, 479.92047119140625], "page": 4, "span": [0, 376], "__ref_s3_data": null}], "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 285.2448425292969, 559.7130737304688, 414.1674499511719], "page": 4, "span": [0, 746], "__ref_s3_data": null}], "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.62298583984375, 98.9438247680664, 559.7176513671875, 282.7770080566406], "page": 4, "span": [0, 1159], "__ref_s3_data": null}], "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 83.13311004638672, 369.2456970214844, 89.64663696289062], "page": 4, "span": [0, 24], "__ref_s3_data": null}], "text": "$^{3}$https://arxiv.org/", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 347.0172424316406, 731.6909790039062], "page": 5, "span": [0, 71], "__ref_s3_data": null}], "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [365.75701904296875, 723.4239501953125, 558.2028198242188, 731.6909790039062], "page": 5, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.79800033569336, 685.2938842773438, 294.04541015625, 704.636474609375], "page": 5, "span": [0, 135], "__ref_s3_data": null}], "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 542.8378295898438, 295.5592346191406, 682.7184448242188], "page": 5, "span": [0, 812], "__ref_s3_data": null}], "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 455.16583251953125, 295.56005859375, 540.2534790039062], "page": 5, "span": [0, 465], "__ref_s3_data": null}], "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.70800018310547, 402.22686767578125, 294.04620361328125, 443.4874572753906], "page": 5, "span": [0, 202], "__ref_s3_data": null}], "text": "- (1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70799255371094, 358.39984130859375, 295.563720703125, 399.6514892578125], "page": 5, "span": [0, 208], "__ref_s3_data": null}], "text": "- (2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 336.4728698730469, 294.0472412109375, 355.81549072265625], "page": 5, "span": [0, 82], "__ref_s3_data": null}], "text": "- (3) For every Caption , there must be exactly one corresponding Picture or Table .", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 314.5648193359375, 294.0459899902344, 333.8984680175781], "page": 5, "span": [0, 70], "__ref_s3_data": null}], "text": "- (4) Connected sub-pictures are grouped together in one Picture object.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 303.59686279296875, 264.5057067871094, 311.98046875], "page": 5, "span": [0, 53], "__ref_s3_data": null}], "text": "- (5) Formula numbers are included in a Formula object.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.7080078125, 270.72882080078125, 294.0461730957031, 301.021484375], "page": 5, "span": [0, 160], "__ref_s3_data": null}], "text": "- (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.52899932861328, 217.798828125, 295.5625305175781, 259.0494689941406], "page": 5, "span": [0, 221], "__ref_s3_data": null}], "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 86.29182434082031, 295.562255859375, 215.3310089111328], "page": 5, "span": [0, 792], "__ref_s3_data": null}], "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 288.11480712890625, 559.8057861328125, 318.5060119628906], "page": 5, "span": [0, 173], "__ref_s3_data": null}], "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [400.12841796875, 331.43994140625, 476.331787109375, 333.5567321777344], "page": 5, "span": [0, 64], "__ref_s3_data": null}], "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.62298583984375, 247.1688232421875, 558.204345703125, 266.5024719238281], "page": 5, "span": [0, 123], "__ref_s3_data": null}], "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.62298583984375, 82.78482818603516, 559.7149047851562, 244.7010040283203], "page": 5, "span": [0, 987], "__ref_s3_data": null}], "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 558.202880859375, 731.6909790039062], "page": 6, "span": [0, 130], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.50199890136719, 608.98291015625, 295.64874267578125, 705.1270751953125], "page": 6, "span": [0, 489], "__ref_s3_data": null}], "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [53.52899932861328, 215.43682861328125, 295.5561218261719, 421.07244873046875], "page": 6, "span": [0, 1252], "__ref_s3_data": null}], "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 193.5609893798828, 147.4853515625, 203.87008666992188], "page": 6, "span": [0, 13], "__ref_s3_data": null}], "text": "5 EXPERIMENTS", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.48400115966797, 82.7008285522461, 295.4281005859375, 178.74644470214844], "page": 6, "span": [0, 584], "__ref_s3_data": null}], "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 449.7158203125, 559.8057861328125, 512.9840087890625], "page": 6, "span": [0, 329], "__ref_s3_data": null}], "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [317.9549865722656, 388.6548156738281, 558.2041625976562, 407.98846435546875], "page": 6, "span": [0, 102], "__ref_s3_data": null}], "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.6409912109375, 311.9428405761719, 558.4364013671875, 386.0704650878906], "page": 6, "span": [0, 397], "__ref_s3_data": null}], "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 284.8690185546875, 466.8532409667969, 295.1781005859375], "page": 6, "span": [0, 30], "__ref_s3_data": null}], "text": "Baselines for Object Detection", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.7489929199219, 85.2998275756836, 558.4308471679688, 279.9754638671875], "page": 6, "span": [0, 1146], "__ref_s3_data": null}], "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 347.0172424316406, 731.6909790039062], "page": 7, "span": [0, 71], "__ref_s3_data": null}], "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [365.75701904296875, 723.4239501953125, 558.2028198242188, 731.6909790039062], "page": 7, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.50199890136719, 663.77685546875, 295.6486511230469, 705.1270751953125], "page": 7, "span": [0, 205], "__ref_s3_data": null}], "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.65899658203125, 663.7767944335938, 559.8068237304688, 705.1270141601562], "page": 7, "span": [0, 189], "__ref_s3_data": null}], "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"prov": [{"bbox": [53.79800033569336, 462.1210021972656, 131.05624389648438, 472.4300842285156], "page": 7, "span": [0, 14], "__ref_s3_data": null}], "text": "Learning Curve", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [52.78499984741211, 262.55181884765625, 295.558349609375, 457.22845458984375], "page": 7, "span": [0, 1157], "__ref_s3_data": null}], "text": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 239.1809844970703, 164.3289794921875, 249.49008178710938], "page": 7, "span": [0, 22], "__ref_s3_data": null}], "text": "Impact of Class Labels", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.46699905395508, 83.44783020019531, 295.5567932128906, 234.2884521484375], "page": 7, "span": [0, 910], "__ref_s3_data": null}], "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/3"}, {"prov": [{"bbox": [317.6860046386719, 375.50982666015625, 559.5849609375, 460.5964660644531], "page": 7, "span": [0, 469], "__ref_s3_data": null}], "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549560546875, 352.2960205078125, 549.860595703125, 362.6051025390625], "page": 7, "span": [0, 46], "__ref_s3_data": null}], "text": "Impact of Document Split in Train and Test Set", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.62298583984375, 196.5628204345703, 559.7138061523438, 347.4034729003906], "page": 7, "span": [0, 852], "__ref_s3_data": null}], "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 173.34898376464844, 418.5477600097656, 183.6580810546875], "page": 7, "span": [0, 18], "__ref_s3_data": null}], "text": "Dataset Comparison", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.6860046386719, 83.35986328125, 559.1881713867188, 168.45645141601562], "page": 7, "span": [0, 521], "__ref_s3_data": null}], "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 558.202880859375, 731.6909790039062], "page": 8, "span": [0, 130], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.50199890136719, 641.85888671875, 295.648681640625, 705.1270751953125], "page": 8, "span": [0, 298], "__ref_s3_data": null}], "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/4"}, {"prov": [{"bbox": [53.79800033569336, 348.85986328125, 294.047119140625, 401.0794677734375], "page": 8, "span": [0, 295], "__ref_s3_data": null}], "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.46699905395508, 206.40382385253906, 295.55908203125, 346.28546142578125], "page": 8, "span": [0, 793], "__ref_s3_data": null}], "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 176.62998962402344, 156.00534057617188, 186.9390869140625], "page": 8, "span": [0, 19], "__ref_s3_data": null}], "text": "Example Predictions", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.52899932861328, 86.64982604980469, 295.5584411621094, 171.7364501953125], "page": 8, "span": [0, 481], "__ref_s3_data": null}], "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.95501708984375, 695.8309936523438, 405.7296142578125, 706.14013671875], "page": 8, "span": [0, 12], "__ref_s3_data": null}], "text": "6 CONCLUSION", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.9549865722656, 605.850830078125, 559.7137451171875, 690.9384765625], "page": 8, "span": [0, 507], "__ref_s3_data": null}], "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.6860046386719, 507.2208251953125, 559.717041015625, 603.2664794921875], "page": 8, "span": [0, 573], "__ref_s3_data": null}], "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.62298583984375, 474.3438415527344, 558.4346923828125, 504.636474609375], "page": 8, "span": [0, 188], "__ref_s3_data": null}], "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 446.5990295410156, 387.3695983886719, 456.9081115722656], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "REFERENCES", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [321.197998046875, 420.8371276855469, 558.2009887695312, 443.29766845703125], "page": 8, "span": [0, 191], "__ref_s3_data": null}], "text": "- [1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. In 2013 12th International Conference on Document Analysis and Recognition , pages 1449-1453, 2013.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [321.197998046875, 388.9571228027344, 559.3798217773438, 419.38763427734375], "page": 8, "span": [0, 279], "__ref_s3_data": null}], "text": "- [2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts rdcl2017. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) , volume 01, pages 1404-1410, 2017.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [321.197998046875, 365.0531005859375, 558.2001342773438, 387.50762939453125], "page": 8, "span": [0, 213], "__ref_s3_data": null}], "text": "- [3] Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and Recognition (cTDaR), April 2019. http://sac.founderit.com/.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [321.197998046875, 333.173095703125, 559.3787231445312, 363.5966491699219], "page": 8, "span": [0, 251], "__ref_s3_data": null}], "text": "- [4] Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on scientific literature parsing. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 605-617. LNCS 12824, SpringerVerlag, sep 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [321.197998046875, 301.2920837402344, 559.0262451171875, 331.7166442871094], "page": 8, "span": [0, 261], "__ref_s3_data": null}], "text": "- [5] Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin, Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis: not dead yet. International Journal on Document Analysis and Recognition (IJDAR) , pages 1-11, 01 2022.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [321.197998046875, 277.3751220703125, 558.20361328125, 299.83563232421875], "page": 8, "span": [0, 235], "__ref_s3_data": null}], "text": "- [6] Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset ever for document layout analysis. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 1015-1022, sep 2019.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [321.1979675292969, 237.53111267089844, 558.9714965820312, 275.9256286621094], "page": 8, "span": [0, 316], "__ref_s3_data": null}], "text": "- [7] Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In Proceedings of the 28th International Conference on Computational Linguistics , COLING, pages 949-960. International Committee on Computational Linguistics, dec 2020.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [321.197998046875, 213.6141357421875, 558.9022216796875, 236.07464599609375], "page": 8, "span": [0, 172], "__ref_s3_data": null}], "text": "- [8] Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction from pdf sources based on rule-based system using integrated formats. In SemWebEval@ESWC , 2016.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [321.197998046875, 181.74110412597656, 559.2744750976562, 212.16464233398438], "page": 8, "span": [0, 271], "__ref_s3_data": null}], "text": "- [9] Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition , CVPR, pages 580-587. IEEE Computer Society, jun 2014.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 165.7931365966797, 558.2020263671875, 180.28463745117188], "page": 8, "span": [0, 149], "__ref_s3_data": null}], "text": "- [10] Ross B. Girshick. Fast R-CNN. In 2015 IEEE International Conference on Computer Vision , ICCV, pages 1440-1448. IEEE Computer Society, dec 2015.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 141.8831329345703, 558.201416015625, 164.3436279296875], "page": 8, "span": [0, 227], "__ref_s3_data": null}], "text": "- [11] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence , 39(6):1137-1149, 2017.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 117.98011016845703, 559.278076171875, 140.43362426757812], "page": 8, "span": [0, 192], "__ref_s3_data": null}], "text": "- [12] Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross B. Girshick. Mask R-CNN. In IEEE International Conference on Computer Vision , ICCV, pages 2980-2988. IEEE Computer Society, Oct 2017.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 86.09910583496094, 558.9715576171875, 116.52364349365234], "page": 8, "span": [0, 305], "__ref_s3_data": null}], "text": "- [13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 347.0172424316406, 731.6909790039062], "page": 9, "span": [0, 71], "__ref_s3_data": null}], "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [365.75701904296875, 723.4239501953125, 558.2028198242188, 731.6909790039062], "page": 9, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [62.323875427246094, 343.73516845703125, 318.5047302246094, 349.7145690917969], "page": 9, "span": [0, 89], "__ref_s3_data": null}], "text": "Text Caption List-Item Formula Table Section-Header Picture Page-Header Page-Footer Title", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [53.79800033569336, 286.16876220703125, 559.807861328125, 327.51800537109375], "page": 9, "span": [0, 386], "__ref_s3_data": null}], "text": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [69.23400115966797, 242.4801025390625, 295.22406005859375, 264.93365478515625], "page": 9, "span": [0, 195], "__ref_s3_data": null}], "text": "Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ultralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow export, opencv dnn support, October 2021.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 218.56314086914062, 295.12176513671875, 241.02362060546875], "page": 9, "span": [0, 190], "__ref_s3_data": null}], "text": "- [14] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. CoRR , abs/2005.12872, 2020.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.79800033569336, 202.62213134765625, 294.042236328125, 217.1136474609375], "page": 9, "span": [0, 132], "__ref_s3_data": null}], "text": "- [15] Mingxing Tan, Ruoming Pang, and Quoc V. Le. Efficientdet: Scalable and efficient object detection. CoRR , abs/1911.09070, 2019.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.798004150390625, 178.71910095214844, 295.2226257324219, 201.17263793945312], "page": 9, "span": [0, 219], "__ref_s3_data": null}], "text": "- [16] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll\u00e1r, and C. Lawrence Zitnick. Microsoft COCO: common objects in context, 2014.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.79800033569336, 162.77911376953125, 295.1200866699219, 177.26263427734375], "page": 9, "span": [0, 100], "__ref_s3_data": null}], "text": "- [17] Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detectron2, 2019.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.79800033569336, 122.92810821533203, 294.80889892578125, 161.3226318359375], "page": 9, "span": [0, 339], "__ref_s3_data": null}], "text": "- [18] Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak, Viktor Kuropiatnyk, Ahmed Nassar, Andre Carvalho, Michele Dolfi, Christoph Auer, Kasper Dinkla, and Peter W. J. Staar. Robust pdf document conversion using recurrent neural networks. In Proceedings of the 35th Conference on Artificial Intelligence , AAAI, pages 1513715145, feb 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.797996520996094, 83.07810974121094, 295.22174072265625, 121.47162628173828], "page": 9, "span": [0, 336], "__ref_s3_data": null}], "text": "- [19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 1192-1200, New York, USA, 2020. Association for Computing Machinery.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 250.45010375976562, 559.0263671875, 264.9336242675781], "page": 9, "span": [0, 153], "__ref_s3_data": null}], "text": "- [20] Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. Vtlayout: Fusion of visual and text features for document layout analysis, 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 226.54010009765625, 558.9714965820312, 248.99362182617188], "page": 9, "span": [0, 188], "__ref_s3_data": null}], "text": "- [21] Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Fei Wu. Vsr: A unified framework for document layout analysis combining vision, semantics and relations, 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 194.65213012695312, 559.275390625, 225.08364868164062], "page": 9, "span": [0, 290], "__ref_s3_data": null}], "text": "- [22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 774-782. ACM, 2018.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 178.71212768554688, 559.3782958984375, 193.20263671875], "page": 9, "span": [0, 138], "__ref_s3_data": null}], "text": "- [23] Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data , 6(1):60, 2019.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}], "figures": [{"prov": [{"bbox": [323.40838623046875, 266.14923095703125, 553.295166015625, 541.6513671875], "page": 1, "span": [0, 84], "__ref_s3_data": null}], "text": "Figure 1: Four examples of complex page layouts across different document categories", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [88.32998657226562, 571.4320068359375, 263.70513916015625, 699.113525390625], "page": 3, "span": [0, 69], "__ref_s3_data": null}], "text": "Figure 2: Distribution of DocLayNet pages across document categories.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [53.05910873413086, 251.1358642578125, 295.8505554199219, 481.2087097167969], "page": 4, "span": [0, 281], "__ref_s3_data": null}], "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [315.960205078125, 332.31915283203125, 559.396484375, 706.6611938476562], "page": 5, "span": [0, 173], "__ref_s3_data": null}], "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [323.4842224121094, 531.9901733398438, 553.5410766601562, 702.1138305664062], "page": 6, "span": [0, 329], "__ref_s3_data": null}], "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [52.964385986328125, 349.8664245605469, 556.9307861328125, 707.2642822265625], "page": 9, "span": [0, 89], "__ref_s3_data": null}], "text": "Text Caption List-Item Formula Table Section-Header Picture Page-Header Page-Footer Title", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [98.93107604980469, 497.91845703125, 512.5799560546875, 654.5245361328125], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. 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\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/2206.01062.md b/tests/data/groundtruth/docling_v1/2206.01062.md
index 29269829..8bbf9d5a 100644
--- a/tests/data/groundtruth/docling_v1/2206.01062.md
+++ b/tests/data/groundtruth/docling_v1/2206.01062.md
@@ -98,21 +98,21 @@ The annotation campaign was carried out in four phases. In phase one, we identif
 
 Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.
 
-|                |         | % of Total   | % of Total   | % of Total   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   |
-|----------------|---------|--------------|--------------|--------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|
-| class label    | Count   | Train        | Test         | Val          | All                                         | Fin                                         | Man                                         | Sci                                         | Law                                         | Pat                                         | Ten                                         |
-| Caption        | 22524   | 2.04         | 1.77         | 2.32         | 84-89                                       | 40-61                                       | 86-92                                       | 94-99                                       | 95-99                                       | 69-78                                       | n/a                                         |
-| Footnote       | 6318    | 0.60         | 0.31         | 0.58         | 83-91                                       | n/a                                         | 100                                         | 62-88                                       | 85-94                                       | n/a                                         | 82-97                                       |
-| Formula        | 25027   | 2.25         | 1.90         | 2.96         | 83-85                                       | n/a                                         | n/a                                         | 84-87                                       | 86-96                                       | n/a                                         | n/a                                         |
-| List-item      | 185660  | 17.19        | 13.34        | 15.82        | 87-88                                       | 74-83                                       | 90-92                                       | 97-97                                       | 81-85                                       | 75-88                                       | 93-95                                       |
-| Page-footer    | 70878   | 6.51         | 5.58         | 6.00         | 93-94                                       | 88-90                                       | 95-96                                       | 100                                         | 92-97                                       | 100                                         | 96-98                                       |
-| Page-header    | 58022   | 5.10         | 6.70         | 5.06         | 85-89                                       | 66-76                                       | 90-94                                       | 98-100                                      | 91-92                                       | 97-99                                       | 81-86                                       |
-| Picture        | 45976   | 4.21         | 2.78         | 5.31         | 69-71                                       | 56-59                                       | 82-86                                       | 69-82                                       | 80-95                                       | 66-71                                       | 59-76                                       |
-| Section-header | 142884  | 12.60        | 15.77        | 12.85        | 83-84                                       | 76-81                                       | 90-92                                       | 94-95                                       | 87-94                                       | 69-73                                       | 78-86                                       |
-| Table          | 34733   | 3.20         | 2.27         | 3.60         | 77-81                                       | 75-80                                       | 83-86                                       | 98-99                                       | 58-80                                       | 79-84                                       | 70-85                                       |
-| Text           | 510377  | 45.82        | 49.28        | 45.00        | 84-86                                       | 81-86                                       | 88-93                                       | 89-93                                       | 87-92                                       | 71-79                                       | 87-95                                       |
-| Title          | 5071    | 0.47         | 0.30         | 0.50         | 60-72                                       | 24-63                                       | 50-63                                       | 94-100                                      | 82-96                                       | 68-79                                       | 24-56                                       |
-| Total          | 1107470 | 941123       | 99816        | 66531        | 82-83                                       | 71-74                                       | 79-81                                       | 89-94                                       | 86-91                                       | 71-76                                       | 68-85                                       |
+|                |         | % of Total   | % of Total   | % of Total   | % of Total   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   |
+|----------------|---------|--------------|--------------|--------------|--------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|
+| class label    | Count   | Train        | Test         | Val          | All          | Fin                                         | Man                                         | Sci                                         | Law                                         | Pat                                         | Ten                                         |
+| Caption        | 22524   | 2.04         | 1.77         | 2.32         | 84-89        | 40-61                                       | 86-92                                       | 94-99                                       | 95-99                                       | 69-78                                       | n/a                                         |
+| Footnote       | 6318    | 0.60         | 0.31         | 0.58         | 83-91        | n/a                                         | 100                                         | 62-88                                       | 85-94                                       | n/a                                         | 82-97                                       |
+| Formula        | 25027   | 2.25         | 1.90         | 2.96         | 83-85        | n/a                                         | n/a                                         | 84-87                                       | 86-96                                       | n/a                                         | n/a                                         |
+| List-item      | 185660  | 17.19        | 13.34        | 15.82        | 87-88        | 74-83                                       | 90-92                                       | 97-97                                       | 81-85                                       | 75-88                                       | 93-95                                       |
+| Page-footer    | 70878   | 6.51         | 5.58         | 6.00         | 93-94        | 88-90                                       | 95-96                                       | 100                                         | 92-97                                       | 100                                         | 96-98                                       |
+| Page-header    | 58022   | 5.10         | 6.70         | 5.06         | 85-89        | 66-76                                       | 90-94                                       | 98-100                                      | 91-92                                       | 97-99                                       | 81-86                                       |
+| Picture        | 45976   | 4.21         | 2.78         | 5.31         | 69-71        | 56-59                                       | 82-86                                       | 69-82                                       | 80-95                                       | 66-71                                       | 59-76                                       |
+| Section-header | 142884  | 12.60        | 15.77        | 12.85        | 83-84        | 76-81                                       | 90-92                                       | 94-95                                       | 87-94                                       | 69-73                                       | 78-86                                       |
+| Table          | 34733   | 3.20         | 2.27         | 3.60         | 77-81        | 75-80                                       | 83-86                                       | 98-99                                       | 58-80                                       | 79-84                                       | 70-85                                       |
+| Text           | 510377  | 45.82        | 49.28        | 45.00        | 84-86        | 81-86                                       | 88-93                                       | 89-93                                       | 87-92                                       | 71-79                                       | 87-95                                       |
+| Title          | 5071    | 0.47         | 0.30         | 0.50         | 60-72        | 24-63                                       | 50-63                                       | 94-100                                      | 82-96                                       | 68-79                                       | 24-56                                       |
+| Total          | 1107470 | 941123       | 99816        | 66531        | 82-83        | 71-74                                       | 79-81                                       | 89-94                                       | 86-91                                       | 71-76                                       | 68-85                                       |
 
 Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.
 <!-- image -->
@@ -212,6 +212,14 @@ Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wis
 | Title          |   77 | Sec.-h. | Sec.-h. | Sec.-h. |
 | Overall        |   72 | 73      | 78      | 77      |
 
+## Learning Curve
+
+One of the fundamental questions related to any dataset is if it is "large enough". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.
+
+## Impact of Class Labels
+
+The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption → Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of
+
 
 
 | Class-count    | 11   | 11   | 5   | 5    |
@@ -230,14 +238,6 @@ Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wis
 | Title          | 77   | 81   |     |      |
 | All            | 72   | 84   | 78  | 87   |
 
-## Learning Curve
-
-One of the fundamental questions related to any dataset is if it is "large enough". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.
-
-## Impact of Class Labels
-
-The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption → Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of
-
 lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.
 
 ## Impact of Document Split in Train and Test Set
@@ -253,19 +253,19 @@ Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network acros
 |                 |            | Testing on   | Testing on   | Testing on   |
 |-----------------|------------|--------------|--------------|--------------|
 | Training on     | labels     | PLN          | DB           | DLN          |
-|                 | Figure     | 96           | 43           | 23           |
-|                 | Sec-header | 87           | -            | 32           |
+| PubLayNet (PLN) | Figure     | 96           | 43           | 23           |
+| PubLayNet (PLN) | Sec-header | 87           | -            | 32           |
 | PubLayNet (PLN) | Table      | 95           | 24           | 49           |
-|                 | Text       | 96           | -            | 42           |
-|                 | total      | 93           | 34           | 30           |
-|                 | Figure     | 77           | 71           | 31           |
+| PubLayNet (PLN) | Text       | 96           | -            | 42           |
+| PubLayNet (PLN) | total      | 93           | 34           | 30           |
+| DocBank (DB)    | Figure     | 77           | 71           | 31           |
 | DocBank (DB)    | Table      | 19           | 65           | 22           |
-|                 | total      | 48           | 68           | 27           |
-|                 | Figure     | 67           | 51           | 72           |
-|                 | Sec-header | 53           | -            | 68           |
+| DocBank (DB)    | total      | 48           | 68           | 27           |
+| DocLayNet (DLN) | Figure     | 67           | 51           | 72           |
+| DocLayNet (DLN) | Sec-header | 53           | -            | 68           |
 | DocLayNet (DLN) | Table      | 87           | 43           | 82           |
-|                 | Text       | 77           | -            | 84           |
-|                 | total      | 59           | 47           | 78           |
+| DocLayNet (DLN) | Text       | 77           | -            | 84           |
+| DocLayNet (DLN) | total      | 59           | 47           | 78           |
 
 Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .
 
diff --git a/tests/data/groundtruth/docling_v1/2206.01062.pages.json b/tests/data/groundtruth/docling_v1/2206.01062.pages.json
index d6baadab..c9a9314e 100644
--- a/tests/data/groundtruth/docling_v1/2206.01062.pages.json
+++ b/tests/data/groundtruth/docling_v1/2206.01062.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for", "bbox": {"l": 107.29999999999998, "t": 83.69470000000013, "r": 505.06195, "b": 99.67058999999995, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Document-Layout Analysis", "bbox": {"l": 200.117, "t": 103.6196900000001, "r": 411.88367, "b": 119.59558000000015, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Birgit Pfitzmann", "bbox": {"l": 102.06001, "t": 133.67236000000003, "r": 182.63805, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "IBM Research", "bbox": {"l": 114.29401000000001, "t": 147.02423, "r": 170.40337, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Rueschlikon, Switzerland", "bbox": {"l": 90.96701, "t": 158.97924999999998, "r": 193.73123, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "bpf@zurich.ibm.com", "bbox": {"l": 100.02301, "t": 170.93524000000002, "r": 184.67522, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Christoph Auer", "bbox": {"l": 268.62402, "t": 133.67236000000003, "r": 344.59933, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "IBM Research", "bbox": {"l": 278.44302, "t": 147.02423, "r": 334.55237, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Rueschlikon, Switzerland", "bbox": {"l": 255.11602999999997, "t": 158.97924999999998, "r": 357.88025, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "cau@zurich.ibm.com", "bbox": {"l": 263.70404, "t": 170.93524000000002, "r": 349.29272, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Michele Dolfi", "bbox": {"l": 437.6930500000001, "t": 133.67236000000003, "r": 503.60208, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "IBM Research", "bbox": {"l": 442.59305000000006, "t": 147.02423, "r": 498.7023899999999, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Rueschlikon, Switzerland", "bbox": {"l": 419.26505, "t": 158.97924999999998, "r": 522.0293, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "dol@zurich.ibm.com", "bbox": {"l": 428.56104000000005, "t": 170.93524000000002, "r": 512.73505, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Ahmed S. Nassar", "bbox": {"l": 182.26804, "t": 192.05737, "r": 265.39255, "b": 203.22357, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "IBM Research", "bbox": {"l": 195.87103, "t": 205.40923999999995, "r": 251.98038999999997, "b": 214.71429, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Rueschlikon, Switzerland", "bbox": {"l": 172.54303, "t": 217.36425999999994, "r": 275.30725, "b": 226.66931, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "ahn@zurich.ibm.com", "bbox": {"l": 180.52803, "t": 229.32025, "r": 267.3222, "b": 238.62531, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Peter Staar", "bbox": {"l": 361.52802, "t": 192.05737, "r": 414.84821, "b": 203.22357, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "IBM Research", "bbox": {"l": 360.02002, "t": 205.40923999999995, "r": 416.12939, "b": 214.71429, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Rueschlikon, Switzerland", "bbox": {"l": 336.69302, "t": 217.36425999999994, "r": 439.45727999999997, "b": 226.66931, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "taa@zurich.ibm.com", "bbox": {"l": 346.20703, "t": 229.32025, "r": 429.94269, "b": 238.62531, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "ABSTRACT", "bbox": {"l": 53.798035, "t": 247.70288000000005, "r": 111.94354, "b": 258.01202, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Accurate document layout analysis is a key requirement for high-", "bbox": {"l": 53.484001, "t": 262.90454, "r": 295.55591, "b": 271.27917, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "quality PDF document conversion. With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "13", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "USING THE VERTICAL TUBE -", "bbox": {"l": 327.83005, "t": 331.57268999999997, "r": 351.16092, "b": 333.31171, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "MODELS AY11230/11234", "bbox": {"l": 327.83005, "t": 333.18292, "r": 348.30536, "b": 334.92194, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, 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The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 239, "label": "text", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 240, "label": "text", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 295, "text": "the landing surface. 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "caption", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 559.51524, "coord_origin": "TOPLEFT"}, "confidence": 0.7793391346931458, "cells": [{"id": 409, "text": "Figure 1: Four examples of complex page layouts across dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458844423294067, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; 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While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "caption", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 559.51524, "coord_origin": "TOPLEFT"}, "confidence": 0.7793391346931458, "cells": [{"id": 409, "text": "Figure 1: Four examples of complex page layouts across dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Four examples of complex page layouts across different document categories"}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458844423294067, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; 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Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. 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Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Washington, DC, USA. 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "caption", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 559.51524, "coord_origin": "TOPLEFT"}, "confidence": 0.7793391346931458, "cells": [{"id": 409, "text": "Figure 1: Four examples of complex page layouts across dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Four examples of complex page layouts across different document categories"}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458844423294067, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; 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\u2022 Applied computing \u2192 Document analysis ; \u2022 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9208475351333618, "cells": [{"id": 411, "text": "KEYWORDS", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KEYWORDS"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 627.00117, "coord_origin": "TOPLEFT"}, "confidence": 0.9509093761444092, "cells": [{"id": 412, "text": "PDF document conversion, layout segmentation, object-detection,", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 616.04218, "coord_origin": "TOPLEFT"}}, {"id": 413, "text": "data set, Machine Learning", "bbox": {"l": 317.95499, "t": 618.62656, "r": 416.94403, "b": 627.00117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning"}, {"label": "text", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "text", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 295.11798, "b": 672.79189, "coord_origin": "TOPLEFT"}, "confidence": 0.7107337117195129, "cells": [{"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.65997, "t": 640.05434, "r": 404.65366, "b": 647.58609, "coord_origin": "TOPLEFT"}, "confidence": 0.8721982836723328, "cells": [{"id": 414, "text": "ACM Reference Format:", "bbox": {"l": 317.65997, "t": 640.05434, "r": 404.65366, "b": 647.58609, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACM Reference Format:"}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 650.11996, "r": 559.5495, "b": 707.377029, "coord_origin": "TOPLEFT"}, "confidence": 0.9455163478851318, "cells": [{"id": 415, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter", "bbox": {"l": 317.95499, "t": 650.11996, "r": 558.35266, "b": 657.56404, "coord_origin": "TOPLEFT"}}, {"id": 416, "text": "Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for Document-", "bbox": {"l": 317.95499, "t": 660.08296, "r": 559.5495, "b": 667.52703, "coord_origin": "TOPLEFT"}}, {"id": 417, "text": "Layout Analysis. In", "bbox": {"l": 317.95499, "t": 670.04497, "r": 383.30807, "b": 677.48904, "coord_origin": "TOPLEFT"}}, {"id": 418, "text": "Proceedings of the 28th ACM SIGKDD Conference on", "bbox": {"l": 385.798, "t": 670.08482, "r": 558.20032, "b": 677.49701, "coord_origin": "TOPLEFT"}}, {"id": 419, "text": "Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Wash-", "bbox": {"l": 317.95499, "t": 680.04781, "r": 559.00092, "b": 687.46001, "coord_origin": "TOPLEFT"}}, {"id": 420, "text": "ington, DC, USA.", "bbox": {"l": 317.95499, "t": 690.01081, "r": 370.11481, "b": 697.423004, "coord_origin": "TOPLEFT"}}, {"id": 421, "text": "ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/", "bbox": {"l": 371.82999, "t": 689.97096, "r": 558.71655, "b": 697.415031, "coord_origin": "TOPLEFT"}}, {"id": 422, "text": "3534678.3539043", "bbox": {"l": 317.95499, "t": 699.932953, "r": 371.59375, "b": 707.377029, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Washington, DC, USA. 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Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680323123931885, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367701411247253, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996682524681091, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279747962952, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813894629478455, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844210147857666, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792873859405518, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347410202026367, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795214533805847, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330971837043762, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019714355469, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594184160232544, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615143537521362, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447574615478516, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749842882156372, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586857914924622, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680323123931885, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367701411247253, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996682524681091, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279747962952, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813894629478455, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844210147857666, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792873859405518, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347410202026367, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795214533805847, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330971837043762, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019714355469, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594184160232544, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615143537521362, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447574615478516, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749842882156372, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586857914924622, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}], "body": [{"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367701411247253, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996682524681091, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279747962952, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813894629478455, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844210147857666, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792873859405518, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347410202026367, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795214533805847, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330971837043762, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019714355469, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594184160232544, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615143537521362, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447574615478516, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749842882156372, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586857914924622, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}], "headers": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680323123931885, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920252323150635, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.91046142578125, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "picture", "bbox": {"l": 88.32998657226562, "t": 92.88650512695312, "r": 263.70513916015625, "b": 220.56797790527344, "coord_origin": "TOPLEFT"}, "confidence": 0.9698249101638794, "cells": [], "children": [{"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425569772720337, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048321843147278, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920252323150635, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.91046142578125, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.32998657226562, "t": 92.88650512695312, "r": 263.70513916015625, "b": 220.56797790527344, "coord_origin": "TOPLEFT"}, "confidence": 0.9698249101638794, "cells": [], "children": [{"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 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"TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "caption", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425569772720337, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \u201cinvisible\u201d tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \u201cinvisible\u201d list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \u201ctext in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048321843147278, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}], "body": [{"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.32998657226562, "t": 92.88650512695312, "r": 263.70513916015625, "b": 220.56797790527344, "coord_origin": "TOPLEFT"}, "confidence": 0.9698249101638794, "cells": [], "children": [{"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "caption", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425569772720337, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \u201cinvisible\u201d tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \u201cinvisible\u201d list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \u201ctext in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048321843147278, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}], "headers": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920252323150635, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.91046142578125, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 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"coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 418.16003, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "95-99", "bbox": {"l": 428.1217, "t": 162.53954999999996, "r": 447.82986, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "69-78", "bbox": {"l": 457.80051, "t": 162.53954999999996, "r": 477.50867, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "n/a", "bbox": {"l": 495.32489, "t": 162.53954999999996, "r": 507.17846999999995, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Footnote", "bbox": {"l": 104.825, "t": 173.49854000000005, "r": 137.3282, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "6318", "bbox": {"l": 182.035, "t": 173.49854000000005, "r": 198.71251, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "0.60", "bbox": {"l": 219.211, "t": 173.49854000000005, "r": 233.69174000000004, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "0.31", "bbox": {"l": 250.01956, "t": 173.49854000000005, "r": 264.50031, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "0.58", "bbox": {"l": 280.82812, "t": 173.49854000000005, "r": 295.30887, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "83-91", "bbox": {"l": 305.27301, "t": 173.49854000000005, "r": 324.98117, "b": 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"coord_origin": "TOPLEFT"}}, {"id": 162, "text": "68-85", "bbox": {"l": 487.47034, "t": 283.48654, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625211477279663, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906538486480713, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 104.825, "t": 140.22351000000003, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 418.16003, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "95-99", "bbox": {"l": 428.1217, "t": 162.53954999999996, "r": 447.82986, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "69-78", "bbox": {"l": 457.80051, "t": 162.53954999999996, "r": 477.50867, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "n/a", "bbox": {"l": 495.32489, "t": 162.53954999999996, "r": 507.17846999999995, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Footnote", "bbox": {"l": 104.825, "t": 173.49854000000005, "r": 137.3282, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "6318", "bbox": {"l": 182.035, "t": 173.49854000000005, "r": 198.71251, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "0.60", "bbox": {"l": 219.211, "t": 173.49854000000005, "r": 233.69174000000004, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "0.31", "bbox": {"l": 250.01956, "t": 173.49854000000005, "r": 264.50031, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "0.58", "bbox": {"l": 280.82812, "t": 173.49854000000005, "r": 295.30887, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "83-91", "bbox": {"l": 305.27301, "t": 173.49854000000005, "r": 324.98117, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "n/a", "bbox": {"l": 342.79739, "t": 173.49854000000005, "r": 354.65097, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "100", "bbox": {"l": 371.81265, "t": 173.49854000000005, "r": 384.32077, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "62-88", "bbox": {"l": 398.45181, "t": 173.49854000000005, "r": 418.15997, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "85-94", "bbox": {"l": 428.12164, "t": 173.49854000000005, "r": 447.8298, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "n/a", "bbox": {"l": 465.655, "t": 173.49854000000005, "r": 477.50857999999994, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "82-97", "bbox": {"l": 487.47025, "t": 173.49854000000005, "r": 507.17841, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Formula", "bbox": {"l": 104.825, "t": 184.45752000000005, "r": 135.33766, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "25027", "bbox": {"l": 177.866, "t": 184.45752000000005, "r": 198.71288, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "2.25", "bbox": {"l": 219.211, "t": 184.45752000000005, "r": 233.69174000000004, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1.90", "bbox": {"l": 250.01956, "t": 184.45752000000005, "r": 264.50031, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "2.96", "bbox": {"l": 280.82812, "t": 184.45752000000005, "r": 295.30887, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "83-85", "bbox": {"l": 305.27301, "t": 184.45752000000005, "r": 324.98117, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "n/a", "bbox": {"l": 342.79739, "t": 184.45752000000005, "r": 354.65097, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "n/a", "bbox": {"l": 372.46719, "t": 184.45752000000005, "r": 384.32077, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "84-87", "bbox": {"l": 398.45181, "t": 184.45752000000005, "r": 418.15997, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "86-96", "bbox": {"l": 428.12164, "t": 184.45752000000005, "r": 447.8298, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "n/a", "bbox": {"l": 465.655, "t": 184.45752000000005, "r": 477.50857999999994, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "n/a", "bbox": {"l": 495.3248, "t": 184.45752000000005, "r": 507.17838000000006, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "List-item", "bbox": {"l": 104.825, 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Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847549200057983, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64874, "b": 606.31924, "coord_origin": "TOPLEFT"}, "confidence": 0.8104852437973022, "cells": [{"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.98039311170578, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.974835991859436, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"1": {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 104.825, "t": 140.22351000000003, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 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"assembled": {"elements": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625211477279663, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906538486480713, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 104.825, "t": 140.22351000000003, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 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4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818442463874817, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847549200057983, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64874, "b": 606.31924, "coord_origin": "TOPLEFT"}, "confidence": 0.8104852437973022, "cells": [{"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.98039311170578, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.974835991859436, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{3}$https://arxiv.org/"}], "body": [{"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906538486480713, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 104.825, "t": 140.22351000000003, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 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"coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 10, "end_col_offset_idx": 11, "text": "71-76", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 487.47034, "t": 283.48654, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 11, "end_col_offset_idx": 12, "text": "68-85", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "picture", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 53.05910873413086, "t": 310.7912902832031, "r": 295.8505554199219, "b": 540.8641357421875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847874045372009, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818442463874817, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847549200057983, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64874, "b": 606.31924, "coord_origin": "TOPLEFT"}, "confidence": 0.8104852437973022, "cells": [{"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.98039311170578, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.974835991859436, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{3}$https://arxiv.org/"}], "headers": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625211477279663, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.887782871723175, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534672856330872, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264117002487183, "cells": [], "children": [{"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, 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"the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691525101661682, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873710632324219, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544305205345154, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611717462539673, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.982954204082489, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.887782871723175, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534672856330872, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264117002487183, "cells": [], "children": [{"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 84, "text": 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326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.902275800704956, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691525101661682, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "text", "id": 22, "page_no": 4, "cluster": {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"label": "caption", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873710632324219, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544305205345154, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611717462539673, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.982954204082489, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other\u2019s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}], "body": [{"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264117002487183, "cells": [], "children": [{"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.902275800704956, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691525101661682, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "text", "id": 22, "page_no": 4, "cluster": {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"label": "caption", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873710632324219, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544305205345154, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611717462539673, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.982954204082489, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other\u2019s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}], "headers": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.887782871723175, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534672856330872, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "human", "bbox": {"l": 132.36501, "t": 197.97351000000003, "r": 157.99098, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "MRCNN", "bbox": {"l": 173.505, "t": 197.97351000000003, "r": 204.61841, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "FRCNN", "bbox": {"l": 220.13028, "t": 197.97351000000003, "r": 248.06958, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "YOLO", "bbox": {"l": 258.03125, "t": 197.97351000000003, "r": 280.17825, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "R50", "bbox": {"l": 168.39301, "t": 208.93255999999997, "r": 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{"id": 68, "text": "74.6", "bbox": {"l": 261.86804, "t": 297.00253, "r": 276.34879, "b": 305.37717, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Table", "bbox": {"l": 67.663002, "t": 307.96155, "r": 87.46978, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "77-81", "bbox": {"l": 135.32401, "t": 307.96155, "r": 155.03215, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "82.2", "bbox": {"l": 167.95399, "t": 307.96155, "r": 182.43472, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "82.9", "bbox": {"l": 194.0462, "t": 307.96155, "r": 208.52695, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "82.2", "bbox": {"l": 226.86324000000002, "t": 307.96155, "r": 241.34396, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "86.3", "bbox": {"l": 261.86804, "t": 307.96155, "r": 276.34879, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Text", "bbox": {"l": 67.663002, "t": 318.91953, "r": 83.623199, "b": 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167.95399, "t": 329.87854, "r": 182.43472, "b": 338.25317, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "80.4", "bbox": {"l": 194.0462, "t": 329.87854, "r": 208.52695, "b": 338.25317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "79.9", "bbox": {"l": 226.86324000000002, "t": 329.87854, "r": 241.34396, "b": 338.25317, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "82.7", "bbox": {"l": 261.86804, "t": 329.87854, "r": 276.34879, "b": 338.25317, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "All", "bbox": {"l": 67.663002, "t": 341.23654, "r": 78.628906, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "82-83", "bbox": {"l": 135.32401, "t": 341.23654, "r": 155.03215, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "72.4", "bbox": {"l": 167.95399, "t": 341.23654, "r": 182.43472, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "73.5", "bbox": {"l": 194.0462, "t": 341.23654, "r": 208.52695, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "73.4", "bbox": {"l": 226.86324000000002, "t": 341.23654, "r": 241.34396, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "76.8", "bbox": {"l": 261.86804, "t": 341.23654, "r": 276.34879, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "0", "bbox": {"l": 349.16577, "t": 246.68017999999995, "r": 352.48175, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "20", "bbox": {"l": 385.93698, "t": 246.68017999999995, "r": 392.56894, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "40", "bbox": {"l": 424.366, "t": 246.68017999999995, "r": 430.99796, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "60", "bbox": {"l": 462.79504000000003, "t": 246.68017999999995, "r": 469.427, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "80", "bbox": {"l": 501.22406, "t": 246.68017999999995, "r": 507.85602, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "100", "bbox": {"l": 537.99524, "t": 246.68017999999995, "r": 547.94318, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "% of DocLayNet training set", "bbox": {"l": 410.28143, "t": 253.80840999999998, "r": 483.47278000000006, "b": 259.88251, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "50", "bbox": {"l": 330.93539, "t": 218.38464, "r": 337.56735, "b": 224.45874000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "55", "bbox": {"l": 330.93539, "t": 192.08660999999995, "r": 337.56735, "b": 198.16071, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "60", "bbox": {"l": 330.93539, "t": 165.78864, "r": 337.56735, "b": 171.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "65", "bbox": {"l": 330.93539, "t": 139.49059999999997, "r": 337.56735, "b": 145.56470000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "70", "bbox": {"l": 330.93539, "t": 113.19263000000001, "r": 337.56735, "b": 119.26671999999996, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "mAP 0.50:0.95", "bbox": {"l": 322.92276, "t": 148.37689, "r": 328.99686, "b": 186.79218000000003, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "10", "bbox": {"l": 470.97235, "t": 235.36676, "r": 477.6055, "b": 241.44086000000004, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "1", "bbox": {"l": 477.65662, "t": 234.82390999999996, "r": 479.97778000000005, "b": 239.07581000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "10", "bbox": {"l": 531.55127, "t": 235.41234999999995, "r": 538.18445, "b": 241.48645, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "2", "bbox": {"l": 538.23553, "t": 234.86951, "r": 540.5567, "b": 239.1214, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "50", "bbox": {"l": 404.91125, "t": 216.00005999999996, "r": 411.54321, "b": 222.07416, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "55", "bbox": {"l": 404.91125, "t": 200.22125000000005, "r": 411.54321, "b": 206.29534999999998, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "60", "bbox": {"l": 404.91125, "t": 184.44244000000003, "r": 411.54321, "b": 190.51653999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "65", "bbox": {"l": 404.91125, "t": 168.66364, "r": 411.54321, "b": 174.73773000000006, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "70", "bbox": {"l": 404.91125, "t": 152.88489000000004, "r": 411.54321, "b": 158.95898, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. 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The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.528999, "t": 370.92755, "r": 295.55612, "b": 576.56317, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579004049301147, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.9876052141189575, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "table", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "table", "bbox": {"l": 67.663002, "t": 197.97351000000003, "r": 280.17825, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}, "confidence": 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"end_col_offset_idx": 6, "text": "76.8", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 317.95499, "t": 279.01599, "r": 559.80579, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.963992178440094, "cells": [{"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.528999, "t": 370.92755, "r": 295.55612, "b": 576.56317, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579004049301147, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.9876052141189575, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}], "body": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64874, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9782734513282776, "cells": [{"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. 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"end_col_offset_idx": 6, "text": "76.8", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 317.95499, "t": 279.01599, "r": 559.80579, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.963992178440094, "cells": [{"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.528999, "t": 370.92755, "r": 295.55612, "b": 576.56317, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579004049301147, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.9876052141189575, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}], "headers": [{"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8662774562835693, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "6", "bbox": {"l": 179.31816, "t": 153.10051999999996, "r": 183.48753, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "5", "bbox": {"l": 213.33669, "t": 153.10051999999996, "r": 217.50606, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "4", "bbox": {"l": 247.35521, "t": 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"b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "72", "bbox": {"l": 151.07401, "t": 285.40454, "r": 159.41275, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "73", "bbox": {"l": 177.23795, "t": 285.40454, "r": 185.57669, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "78", "bbox": {"l": 211.25645, "t": 285.40454, "r": 219.5952, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "77", "bbox": {"l": 245.27496, "t": 285.40454, "r": 253.61371, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Class-count", "bbox": {"l": 358.63901, "t": 153.10051999999996, "r": 401.73154, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "11", "bbox": {"l": 440.22501, "t": 153.10051999999996, "r": 448.56375, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "5", "bbox": {"l": 494.38, "t": 153.10051999999996, "r": 498.54938, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Split", "bbox": {"l": 358.63901, "t": 164.05951000000005, "r": 375.27167, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Doc", "bbox": {"l": 423.341, "t": 164.05951000000005, "r": 438.0459, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Page", 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460.90115000000003, "b": 282.42117, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "84", "bbox": {"l": 478.59399, "t": 274.04657, "r": 486.93274, "b": 282.42117, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "90", "bbox": {"l": 504.6324200000001, "t": 274.04657, "r": 512.97119, "b": 282.42117, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Title", "bbox": {"l": 358.63901, "t": 285.00552, "r": 375.63034, "b": 293.38015999999993, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "77", "bbox": {"l": 426.52399, "t": 285.00552, "r": 434.86273, "b": 293.38015999999993, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "81", "bbox": {"l": 452.56240999999994, "t": 285.00552, "r": 460.90115000000003, "b": 293.38015999999993, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "All", "bbox": {"l": 358.63901, "t": 296.36255, "r": 369.60492, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "72", "bbox": {"l": 426.52399, "t": 296.36255, "r": 434.86273, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "84", "bbox": {"l": 452.56240999999994, "t": 296.36255, "r": 460.90115000000003, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "78", "bbox": {"l": 478.59399, "t": 296.36255, "r": 486.93274, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "87", "bbox": {"l": 504.6324200000001, "t": 296.36255, "r": 512.97119, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9316117763519287, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.8296932578086853, "cells": [{"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.87362140417099, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 261.46832, "b": 293.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9869412779808044, "cells": [{"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", 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"coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}, "confidence": 0.9555687308311462, "cells": [{"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740145802497864, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572024941444397, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589216113090515, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"4": {"label": "table", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "table", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 261.46832, "b": 293.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9869412779808044, "cells": [{"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, 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131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}, "confidence": 0.9555687308311462, "cells": [{"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Learning Curve"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740145802497864, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "text", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is \u201clarge enough\u201d. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572024941444397, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589216113090515, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "body": [{"label": "text", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.8296932578086853, "cells": [{"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels."}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.87362140417099, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement."}, {"label": "table", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "table", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 261.46832, "b": 293.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9869412779808044, "cells": [{"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "6", "bbox": {"l": 179.31816, "t": 153.10051999999996, "r": 183.48753, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "5", "bbox": {"l": 213.33669, "t": 153.10051999999996, "r": 217.50606, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "4", "bbox": {"l": 247.35521, "t": 153.10051999999996, "r": 251.52458, "b": 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131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}, "confidence": 0.9555687308311462, "cells": [{"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Learning Curve"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740145802497864, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "text", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is \u201clarge enough\u201d. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572024941444397, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589216113090515, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "headers": [{"label": "page_header", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9316117763519287, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.32153, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 295.64868, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "cLayNet data-sets. By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Testing on", "bbox": {"l": 217.74099999999999, "t": 175.01855, "r": 256.26065, "b": 183.39319, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Training on", "bbox": {"l": 89.954002, "t": 185.97655999999995, "r": 133.24379, "b": 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{"id": 30, "text": "42", "bbox": {"l": 256.49792, "t": 230.21155, "r": 264.83667, "b": 238.58618, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "total", "bbox": {"l": 154.629, "t": 241.16956000000005, "r": 171.2796, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "93", "bbox": {"l": 208.44701, "t": 241.16956000000005, "r": 216.78575000000004, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "34", "bbox": {"l": 232.1183, "t": 241.16956000000005, "r": 240.45705, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "30", "bbox": {"l": 256.49792, "t": 241.16956000000005, "r": 264.83667, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "DocBank (DB)", "bbox": {"l": 78.530998, "t": 263.48650999999995, "r": 131.19963, "b": 271.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Figure", "bbox": {"l": 154.629, "t": 252.52752999999996, "r": 177.92371, "b": 260.90216, "coord_origin": 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"coord_origin": "TOPLEFT"}}, {"id": 44, "text": "total", "bbox": {"l": 154.629, "t": 274.44556, "r": 171.2796, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "48", "bbox": {"l": 208.44701, "t": 274.44556, "r": 216.78575000000004, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "68", "bbox": {"l": 232.1183, "t": 274.44556, "r": 240.45705, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "27", "bbox": {"l": 256.49792, "t": 274.44556, "r": 264.83667, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "DocLayNet (DLN)", "bbox": {"l": 78.530998, "t": 307.72055, "r": 144.66716, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Figure", "bbox": {"l": 154.629, "t": 285.80255, "r": 177.92371, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "67", "bbox": {"l": 208.44701, "t": 285.80255, "r": 216.78575000000004, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "51", "bbox": {"l": 232.1183, "t": 285.80255, "r": 240.45705, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "72", "bbox": {"l": 256.49792, "t": 285.80255, "r": 264.83667, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Sec-header", "bbox": {"l": 154.629, "t": 296.76154, "r": 194.72675, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "53", "bbox": {"l": 208.44701, "t": 296.76154, "r": 216.78575000000004, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "-", "bbox": {"l": 234.77235, "t": 296.76154, "r": 237.80299000000002, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "68", "bbox": {"l": 256.49792, "t": 296.76154, "r": 264.83667, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Table", "bbox": {"l": 154.629, "t": 307.72055, "r": 174.43578, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "87", "bbox": {"l": 208.44701, "t": 307.72055, "r": 216.78575000000004, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "43", "bbox": {"l": 232.1183, "t": 307.72055, "r": 240.45705, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "82", "bbox": {"l": 256.49792, "t": 307.72055, "r": 264.83667, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "Text", "bbox": {"l": 154.629, "t": 318.67953, "r": 170.58919, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "77", "bbox": {"l": 208.44701, "t": 318.67953, "r": 216.78575000000004, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "-", "bbox": {"l": 234.77235, "t": 318.67953, "r": 237.80299000000002, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "84", "bbox": {"l": 256.49792, "t": 318.67953, "r": 264.83667, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "total", "bbox": {"l": 154.629, "t": 329.63855, "r": 171.2796, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "59", "bbox": {"l": 208.44701, "t": 329.63855, "r": 216.78575000000004, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "47", "bbox": {"l": 232.1183, "t": 329.63855, "r": 240.45705, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "78", "bbox": {"l": 256.49792, "t": 329.63855, "r": 264.83667, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319990873336792, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64868, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9824119210243225, "cells": [{"id": 2, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.32153, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 295.64868, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "cLayNet data-sets. By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. 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In", "bbox": {"l": 333.39099, "t": 388.55236999999994, "r": 367.4339, "b": 395.06592, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "2017 14th IAPR International Conference on Document Analysis and", "bbox": {"l": 369.17401, "t": 388.58725000000004, "r": 558.20422, "b": 395.07288, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Recognition (ICDAR)", "bbox": {"l": 333.39099, "t": 396.55725, "r": 390.87601, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": ", volume 01, pages 1404-1410, 2017.", "bbox": {"l": 390.87698, "t": 396.52237, "r": 492.17831, "b": 403.03592, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 390.92053, "r": 294.04712, "b": 443.14014, "coord_origin": "TOPLEFT"}, "confidence": 0.9839397668838501, "cells": [{"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 321.198, "t": 404.49237, "r": 558.20013, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9257484078407288, "cells": [{"id": 148, "text": "[3]", "bbox": {"l": 321.198, "t": 404.49237, "r": 329.53583, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian", "bbox": {"l": 331.30682, "t": 404.49237, "r": 558.19958, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and", "bbox": {"l": 333.39099, "t": 412.46335, "r": 558.20013, "b": 418.9769, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "Recognition (cTDaR), April 2019. http://sac.founderit.com/.", "bbox": {"l": 333.39099, "t": 420.43335, "r": 501.80127, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "list_item", "bbox": {"l": 321.198, "t": 428.40335, "r": 559.37872, "b": 458.8269, "coord_origin": "TOPLEFT"}, "confidence": 0.9270963668823242, "cells": [{"id": 152, "text": "[4]", "bbox": {"l": 321.198, "t": 428.40335, "r": 329.91299, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on", "bbox": {"l": 331.7641, "t": 428.40335, "r": 558.19885, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "scientific literature parsing. In", "bbox": {"l": 333.39099, "t": 436.3733500000001, "r": 423.77225, "b": 442.8869, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Proceedings of the International Conference on", "bbox": {"l": 425.909, "t": 436.40823, "r": 558.20178, "b": 442.89386, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 444.37823, "r": 429.42697, "b": 450.86386, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": ", ICDAR, pages 605-617. LNCS 12824, Springer-", "bbox": {"l": 429.42400999999995, "t": 444.34335, "r": 559.37872, "b": 450.85689999999994, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Verlag, sep 2021.", "bbox": {"l": 332.819, "t": 452.31335, "r": 380.01764, "b": 458.8269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "list_item", "bbox": {"l": 321.198, "t": 460.28336, "r": 559.02625, "b": 490.70792, "coord_origin": "TOPLEFT"}, "confidence": 0.9289959073066711, "cells": [{"id": 159, "text": "[5]", "bbox": {"l": 321.198, "t": 460.28336, "r": 329.22977, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin,", "bbox": {"l": 330.93576, "t": 460.28336, "r": 558.97156, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis:", "bbox": {"l": 333.39099, "t": 468.25336, "r": 559.02625, "b": 474.76691, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "not dead yet.", "bbox": {"l": 333.39099, "t": 476.22437, "r": 368.85431, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "International Journal on Document Analysis and Recognition (IJDAR)", "bbox": {"l": 370.811, "t": 476.25925, "r": 557.46326, "b": 482.74487, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": ",", "bbox": {"l": 557.46503, "t": 476.22437, "r": 558.96857, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "pages 1-11, 01 2022.", "bbox": {"l": 333.39099, "t": 484.19437, "r": 390.82715, "b": 490.70792, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "list_item", "bbox": {"l": 321.198, "t": 492.16437, "r": 558.20361, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.927467405796051, "cells": [{"id": 166, "text": "[6]", "bbox": {"l": 321.198, "t": 492.16437, "r": 329.42145, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset", "bbox": {"l": 331.16812, "t": 492.16437, "r": 558.20361, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "ever for document layout analysis. In", "bbox": {"l": 333.39099, "t": 500.13437, "r": 438.6101100000001, "b": 506.64792, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "Proceedings of the International Conference", "bbox": {"l": 440.349, "t": 500.16925, "r": 558.19958, "b": 506.65488, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "on Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 508.13925, "r": 441.40118, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": ", ICDAR, pages 1015-1022, sep 2019.", "bbox": {"l": 441.4019799999999, "t": 508.10437, "r": 544.78162, "b": 514.61792, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 321.19797, "t": 516.07437, "r": 558.9715, "b": 554.46889, "coord_origin": "TOPLEFT"}, "confidence": 0.9426887035369873, "cells": [{"id": 172, "text": "[7]", "bbox": {"l": 321.19797, "t": 516.07437, "r": 329.74161, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and", "bbox": {"l": 331.55634, "t": 516.07437, "r": 558.19897, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In", "bbox": {"l": 333.39099, "t": 524.0443700000001, "r": 558.19891, "b": 530.55792, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "Proceedings of the 28th International Conference on Computational Linguistics", "bbox": {"l": 333.39099, "t": 532.04922, "r": 557.40228, "b": 538.53487, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": ",", "bbox": {"l": 557.40399, "t": 532.01437, "r": 558.96893, "b": 538.5278900000001, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "COLING, pages 949-960. International Committee on Computational Linguistics,", "bbox": {"l": 333.39099, "t": 539.98438, "r": 558.9715, "b": 546.49789, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "dec 2020.", "bbox": {"l": 333.39099, "t": 547.95535, "r": 359.31955, "b": 554.46889, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "list_item", "bbox": {"l": 321.198, "t": 555.92535, "r": 558.90222, "b": 578.38586, "coord_origin": "TOPLEFT"}, "confidence": 0.9139776229858398, "cells": [{"id": 179, "text": "[8]", "bbox": {"l": 321.198, "t": 555.92535, "r": 329.7088, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction", "bbox": {"l": 331.51654, "t": 555.92535, "r": 558.19891, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "from pdf sources based on rule-based system using integrated formats. In", "bbox": {"l": 333.39099, "t": 563.89536, "r": 535.54352, "b": 570.40889, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "SemWe-", "bbox": {"l": 536.96399, "t": 563.93024, "r": 558.90222, "b": 570.4158600000001, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "bEval@ESWC", "bbox": {"l": 333.39099, "t": 571.9002399999999, "r": 371.94217, "b": 578.38586, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": ", 2016.", "bbox": {"l": 371.94299, "t": 571.86536, "r": 389.72617, "b": 578.3788900000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "list_item", "bbox": {"l": 321.198, "t": 579.83536, "r": 559.27448, "b": 610.2589, "coord_origin": "TOPLEFT"}, "confidence": 0.9113092422485352, "cells": [{"id": 185, "text": "[9]", "bbox": {"l": 321.198, "t": 579.83536, "r": 329.45999, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature", "bbox": {"l": 331.21487, "t": 579.83536, "r": 558.19843, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "hierarchies for accurate object detection and semantic segmentation. In", "bbox": {"l": 333.39099, "t": 587.8053600000001, "r": 543.05487, "b": 594.31889, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "IEEE", "bbox": {"l": 544.98499, "t": 587.84024, "r": 558.20148, "b": 594.32587, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Conference on Computer Vision and Pattern Recognition", "bbox": {"l": 333.39099, "t": 595.81024, "r": 491.61166, "b": 602.29587, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": ", CVPR, pages 580-587.", "bbox": {"l": 491.61301, "t": 595.77536, "r": 559.27448, "b": 602.28889, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "IEEE Computer Society, jun 2014.", "bbox": {"l": 333.39099, "t": 603.74536, "r": 428.59726000000006, "b": 610.2589, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574695229530334, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "list_item", "bbox": {"l": 317.95499, "t": 611.71536, "r": 558.20203, "b": 626.20686, "coord_origin": "TOPLEFT"}, "confidence": 0.9028682708740234, "cells": [{"id": 192, "text": "[10]", "bbox": {"l": 317.95499, "t": 611.71536, "r": 329.54767, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Ross B. Girshick. Fast R-CNN. In", "bbox": {"l": 331.31268, "t": 611.71536, "r": 425.1554, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "2015 IEEE International Conference on Computer", "bbox": {"l": 426.77802, "t": 611.75024, "r": 558.20203, "b": 618.23587, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Vision", "bbox": {"l": 333.39099, "t": 619.72124, "r": 350.59537, "b": 626.20686, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": ", ICCV, pages 1440-1448. IEEE Computer Society, dec 2015.", "bbox": {"l": 350.59598, "t": 619.68637, "r": 518.58777, "b": 626.19989, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859862327575684, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "list_item", "bbox": {"l": 317.95499, "t": 627.65637, "r": 558.20142, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9127580523490906, "cells": [{"id": 197, "text": "[11]", "bbox": {"l": 317.95499, "t": 627.65637, "r": 329.5459, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 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By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. 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Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. 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"end_col_offset_idx": 4, "text": "47", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 256.49792, "t": 329.63855, "r": 264.83667, "b": 338.01318, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "78", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9800511598587036, "cells": [{"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9676095843315125, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table", "bbox": {"l": 331.53516, "t": 348.70233, "r": 558.19904, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "competition. In", "bbox": {"l": 333.39099, "t": 356.67236, "r": 379.36414, "b": 363.18591, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "2013 12th International Conference on Document Analysis and", "bbox": {"l": 381.37201, "t": 356.70724, "r": 558.20099, "b": 363.19287, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Recognition", "bbox": {"l": 333.39099, "t": 364.67724999999996, "r": 365.59601, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ", pages 1449-1453, 2013.", "bbox": {"l": 365.59601, "t": 364.64236, "r": 434.29489, "b": 371.15591, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. In 2013 12th International Conference on Document Analysis and Recognition , pages 1449-1453, 2013."}, {"label": "list_item", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 321.198, "t": 372.61237, "r": 559.37982, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9480941295623779, "cells": [{"id": 141, "text": "[2]", "bbox": {"l": 321.198, "t": 372.61237, "r": 329.85956, "b": 379.12592, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Ic-", "bbox": {"l": 331.69931, "t": 372.61237, "r": 559.37976, "b": 379.12592, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "dar2017 competition on recognition of documents with complex layouts -", "bbox": {"l": 333.39099, "t": 380.58237, "r": 559.37982, "b": 387.09592, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "rdcl2017. In", "bbox": {"l": 333.39099, "t": 388.55236999999994, "r": 367.4339, "b": 395.06592, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "2017 14th IAPR International Conference on Document Analysis and", "bbox": {"l": 369.17401, "t": 388.58725000000004, "r": 558.20422, "b": 395.07288, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Recognition (ICDAR)", "bbox": {"l": 333.39099, "t": 396.55725, "r": 390.87601, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": ", volume 01, pages 1404-1410, 2017.", "bbox": {"l": 390.87698, "t": 396.52237, "r": 492.17831, "b": 403.03592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts rdcl2017. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) , volume 01, pages 1404-1410, 2017."}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 390.92053, "r": 294.04712, "b": 443.14014, "coord_origin": "TOPLEFT"}, "confidence": 0.9839397668838501, "cells": [{"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet\u2019s other labels as specified in table 3, and also PubLayNet\u2019s List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "list_item", "id": 17, "page_no": 7, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 321.198, "t": 404.49237, "r": 558.20013, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9257484078407288, "cells": [{"id": 148, "text": "[3]", "bbox": {"l": 321.198, "t": 404.49237, "r": 329.53583, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian", "bbox": {"l": 331.30682, "t": 404.49237, "r": 558.19958, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and", "bbox": {"l": 333.39099, "t": 412.46335, "r": 558.20013, "b": 418.9769, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "Recognition (cTDaR), April 2019. http://sac.founderit.com/.", "bbox": {"l": 333.39099, "t": 420.43335, "r": 501.80127, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[3] Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and Recognition (cTDaR), April 2019. http://sac.founderit.com/."}, {"label": "list_item", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "list_item", "bbox": {"l": 321.198, "t": 428.40335, "r": 559.37872, "b": 458.8269, "coord_origin": "TOPLEFT"}, "confidence": 0.9270963668823242, "cells": [{"id": 152, "text": "[4]", "bbox": {"l": 321.198, "t": 428.40335, "r": 329.91299, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on", "bbox": {"l": 331.7641, "t": 428.40335, "r": 558.19885, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "scientific literature parsing. In", "bbox": {"l": 333.39099, "t": 436.3733500000001, "r": 423.77225, "b": 442.8869, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Proceedings of the International Conference on", "bbox": {"l": 425.909, "t": 436.40823, "r": 558.20178, "b": 442.89386, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 444.37823, "r": 429.42697, "b": 450.86386, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": ", ICDAR, pages 605-617. LNCS 12824, Springer-", "bbox": {"l": 429.42400999999995, "t": 444.34335, "r": 559.37872, "b": 450.85689999999994, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Verlag, sep 2021.", "bbox": {"l": 332.819, "t": 452.31335, "r": 380.01764, "b": 458.8269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[4] Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on scientific literature parsing. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 605-617. LNCS 12824, SpringerVerlag, sep 2021."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "list_item", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 321.198, "t": 460.28336, "r": 559.02625, "b": 490.70792, "coord_origin": "TOPLEFT"}, "confidence": 0.9289959073066711, "cells": [{"id": 159, "text": "[5]", "bbox": {"l": 321.198, "t": 460.28336, "r": 329.22977, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin,", "bbox": {"l": 330.93576, "t": 460.28336, "r": 558.97156, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis:", "bbox": {"l": 333.39099, "t": 468.25336, "r": 559.02625, "b": 474.76691, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "not dead yet.", "bbox": {"l": 333.39099, "t": 476.22437, "r": 368.85431, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "International Journal on Document Analysis and Recognition (IJDAR)", "bbox": {"l": 370.811, "t": 476.25925, "r": 557.46326, "b": 482.74487, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": ",", "bbox": {"l": 557.46503, "t": 476.22437, "r": 558.96857, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "pages 1-11, 01 2022.", "bbox": {"l": 333.39099, "t": 484.19437, "r": 390.82715, "b": 490.70792, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[5] Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin, Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis: not dead yet. International Journal on Document Analysis and Recognition (IJDAR) , pages 1-11, 01 2022."}, {"label": "list_item", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 321.198, "t": 492.16437, "r": 558.20361, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.927467405796051, "cells": [{"id": 166, "text": "[6]", "bbox": {"l": 321.198, "t": 492.16437, "r": 329.42145, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset", "bbox": {"l": 331.16812, "t": 492.16437, "r": 558.20361, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "ever for document layout analysis. In", "bbox": {"l": 333.39099, "t": 500.13437, "r": 438.6101100000001, "b": 506.64792, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "Proceedings of the International Conference", "bbox": {"l": 440.349, "t": 500.16925, "r": 558.19958, "b": 506.65488, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "on Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 508.13925, "r": 441.40118, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": ", ICDAR, pages 1015-1022, sep 2019.", "bbox": {"l": 441.4019799999999, "t": 508.10437, "r": 544.78162, "b": 514.61792, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[6] Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset ever for document layout analysis. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 1015-1022, sep 2019."}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 321.19797, "t": 516.07437, "r": 558.9715, "b": 554.46889, "coord_origin": "TOPLEFT"}, "confidence": 0.9426887035369873, "cells": [{"id": 172, "text": "[7]", "bbox": {"l": 321.19797, "t": 516.07437, "r": 329.74161, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and", "bbox": {"l": 331.55634, "t": 516.07437, "r": 558.19897, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In", "bbox": {"l": 333.39099, "t": 524.0443700000001, "r": 558.19891, "b": 530.55792, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "Proceedings of the 28th International Conference on Computational Linguistics", "bbox": {"l": 333.39099, "t": 532.04922, "r": 557.40228, "b": 538.53487, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": ",", "bbox": {"l": 557.40399, "t": 532.01437, "r": 558.96893, "b": 538.5278900000001, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "COLING, pages 949-960. International Committee on Computational Linguistics,", "bbox": {"l": 333.39099, "t": 539.98438, "r": 558.9715, "b": 546.49789, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "dec 2020.", "bbox": {"l": 333.39099, "t": 547.95535, "r": 359.31955, "b": 554.46889, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[7] Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In Proceedings of the 28th International Conference on Computational Linguistics , COLING, pages 949-960. International Committee on Computational Linguistics, dec 2020."}, {"label": "list_item", "id": 19, "page_no": 7, "cluster": {"id": 19, "label": "list_item", "bbox": {"l": 321.198, "t": 555.92535, "r": 558.90222, "b": 578.38586, "coord_origin": "TOPLEFT"}, "confidence": 0.9139776229858398, "cells": [{"id": 179, "text": "[8]", "bbox": {"l": 321.198, "t": 555.92535, "r": 329.7088, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction", "bbox": {"l": 331.51654, "t": 555.92535, "r": 558.19891, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "from pdf sources based on rule-based system using integrated formats. In", "bbox": {"l": 333.39099, "t": 563.89536, "r": 535.54352, "b": 570.40889, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "SemWe-", "bbox": {"l": 536.96399, "t": 563.93024, "r": 558.90222, "b": 570.4158600000001, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "bEval@ESWC", "bbox": {"l": 333.39099, "t": 571.9002399999999, "r": 371.94217, "b": 578.38586, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": ", 2016.", "bbox": {"l": 371.94299, "t": 571.86536, "r": 389.72617, "b": 578.3788900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[8] Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction from pdf sources based on rule-based system using integrated formats. In SemWebEval@ESWC , 2016."}, {"label": "list_item", "id": 21, "page_no": 7, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 321.198, "t": 579.83536, "r": 559.27448, "b": 610.2589, "coord_origin": "TOPLEFT"}, "confidence": 0.9113092422485352, "cells": [{"id": 185, "text": "[9]", "bbox": {"l": 321.198, "t": 579.83536, "r": 329.45999, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature", "bbox": {"l": 331.21487, "t": 579.83536, "r": 558.19843, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "hierarchies for accurate object detection and semantic segmentation. In", "bbox": {"l": 333.39099, "t": 587.8053600000001, "r": 543.05487, "b": 594.31889, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "IEEE", "bbox": {"l": 544.98499, "t": 587.84024, "r": 558.20148, "b": 594.32587, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Conference on Computer Vision and Pattern Recognition", "bbox": {"l": 333.39099, "t": 595.81024, "r": 491.61166, "b": 602.29587, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": ", CVPR, pages 580-587.", "bbox": {"l": 491.61301, "t": 595.77536, "r": 559.27448, "b": 602.28889, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "IEEE Computer Society, jun 2014.", "bbox": {"l": 333.39099, "t": 603.74536, "r": 428.59726000000006, "b": 610.2589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[9] Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. 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Fast R-CNN. In", "bbox": {"l": 331.31268, "t": 611.71536, "r": 425.1554, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "2015 IEEE International Conference on Computer", "bbox": {"l": 426.77802, "t": 611.75024, "r": 558.20203, "b": 618.23587, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Vision", "bbox": {"l": 333.39099, "t": 619.72124, "r": 350.59537, "b": 626.20686, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": ", ICCV, pages 1440-1448. IEEE Computer Society, dec 2015.", "bbox": {"l": 350.59598, "t": 619.68637, "r": 518.58777, "b": 626.19989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[10] Ross B. Girshick. Fast R-CNN. In 2015 IEEE International Conference on Computer Vision , ICCV, pages 1440-1448. IEEE Computer Society, dec 2015."}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859862327575684, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "list_item", "id": 20, "page_no": 7, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 317.95499, "t": 627.65637, "r": 558.20142, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9127580523490906, "cells": [{"id": 197, "text": "[11]", "bbox": {"l": 317.95499, "t": 627.65637, "r": 329.5459, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards", "bbox": {"l": 331.31064, "t": 627.65637, "r": 558.20142, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "real-time object detection with region proposal networks.", "bbox": {"l": 333.39099, "t": 635.62637, "r": 497.50909, "b": 642.13989, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "IEEE Transactions on", "bbox": {"l": 500.01401, "t": 635.66124, "r": 558.19885, "b": 642.14687, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Pattern Analysis and Machine Intelligence", "bbox": {"l": 333.39099, "t": 643.6312399999999, "r": 449.38620000000003, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": ", 39(6):1137-1149, 2017.", "bbox": {"l": 449.38699, "t": 643.59637, "r": 515.74268, "b": 650.10989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[11] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence , 39(6):1137-1149, 2017."}, {"label": "list_item", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 317.95499, "t": 651.56638, "r": 559.27808, "b": 674.01989, "coord_origin": "TOPLEFT"}, "confidence": 0.9142336249351501, "cells": [{"id": 203, "text": "[12]", "bbox": {"l": 317.95499, "t": 651.56638, "r": 329.41763, "b": 658.0799, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross B. Girshick. Mask R-CNN.", "bbox": {"l": 331.16287, "t": 651.56638, "r": 559.27808, "b": 658.0799, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "In", "bbox": {"l": 333.39099, "t": 659.53638, "r": 339.35904, "b": 666.0499, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "IEEE International Conference on Computer Vision", "bbox": {"l": 341.56299, "t": 659.57124, "r": 485.8273, "b": 666.05687, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ", ICCV, pages 2980-2988.", "bbox": {"l": 485.82901, "t": 659.53638, "r": 559.27356, "b": 666.0499, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "IEEE Computer Society, Oct 2017.", "bbox": {"l": 333.39099, "t": 667.50636, "r": 429.30161000000004, "b": 674.01989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[12] Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross B. Girshick. Mask R-CNN. In IEEE International Conference on Computer Vision , ICCV, pages 2980-2988. IEEE Computer Society, Oct 2017."}, {"label": "list_item", "id": 23, "page_no": 7, "cluster": {"id": 23, "label": "list_item", "bbox": {"l": 317.95499, "t": 675.47636, "r": 558.97156, "b": 705.900894, "coord_origin": "TOPLEFT"}, "confidence": 0.8895393013954163, "cells": [{"id": 209, "text": "[13]", "bbox": {"l": 317.95499, "t": 675.47636, "r": 330.11407, "b": 681.9898900000001, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012,", "bbox": {"l": 331.96533, "t": 675.47636, "r": 558.96716, "b": 681.9898900000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "bbox": {"l": 333.18201, "t": 683.44637, "r": 558.96661, "b": 689.95989, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "bbox": {"l": 333.39099, "t": 691.41737, "r": 558.97156, "b": 697.930893, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "bbox": {"l": 333.39099, "t": 699.387367, "r": 558.20001, "b": 705.900894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu"}], "body": [{"label": "section_header", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319990873336792, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 CONCLUSION"}, {"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64868, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9824119210243225, "cells": [{"id": 2, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.32153, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 295.64868, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "cLayNet data-sets. By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. 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We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9676095843315125, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table", "bbox": {"l": 331.53516, "t": 348.70233, "r": 558.19904, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "competition. In", "bbox": {"l": 333.39099, "t": 356.67236, "r": 379.36414, "b": 363.18591, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "2013 12th International Conference on Document Analysis and", "bbox": {"l": 381.37201, "t": 356.70724, "r": 558.20099, "b": 363.19287, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Recognition", "bbox": {"l": 333.39099, "t": 364.67724999999996, "r": 365.59601, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ", pages 1449-1453, 2013.", "bbox": {"l": 365.59601, "t": 364.64236, "r": 434.29489, "b": 371.15591, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. 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In", "bbox": {"l": 333.39099, "t": 388.55236999999994, "r": 367.4339, "b": 395.06592, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "2017 14th IAPR International Conference on Document Analysis and", "bbox": {"l": 369.17401, "t": 388.58725000000004, "r": 558.20422, "b": 395.07288, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Recognition (ICDAR)", "bbox": {"l": 333.39099, "t": 396.55725, "r": 390.87601, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": ", volume 01, pages 1404-1410, 2017.", "bbox": {"l": 390.87698, "t": 396.52237, "r": 492.17831, "b": 403.03592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts rdcl2017. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) , volume 01, pages 1404-1410, 2017."}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 390.92053, "r": 294.04712, "b": 443.14014, "coord_origin": "TOPLEFT"}, "confidence": 0.9839397668838501, "cells": [{"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet\u2019s other labels as specified in table 3, and also PubLayNet\u2019s List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "list_item", "id": 17, "page_no": 7, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 321.198, "t": 404.49237, "r": 558.20013, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9257484078407288, "cells": [{"id": 148, "text": "[3]", "bbox": {"l": 321.198, "t": 404.49237, "r": 329.53583, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian", "bbox": {"l": 331.30682, "t": 404.49237, "r": 558.19958, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and", "bbox": {"l": 333.39099, "t": 412.46335, "r": 558.20013, "b": 418.9769, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "Recognition (cTDaR), April 2019. http://sac.founderit.com/.", "bbox": {"l": 333.39099, "t": 420.43335, "r": 501.80127, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[3] Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and Recognition (cTDaR), April 2019. http://sac.founderit.com/."}, {"label": "list_item", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "list_item", "bbox": {"l": 321.198, "t": 428.40335, "r": 559.37872, "b": 458.8269, "coord_origin": "TOPLEFT"}, "confidence": 0.9270963668823242, "cells": [{"id": 152, "text": "[4]", "bbox": {"l": 321.198, "t": 428.40335, "r": 329.91299, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on", "bbox": {"l": 331.7641, "t": 428.40335, "r": 558.19885, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "scientific literature parsing. In", "bbox": {"l": 333.39099, "t": 436.3733500000001, "r": 423.77225, "b": 442.8869, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Proceedings of the International Conference on", "bbox": {"l": 425.909, "t": 436.40823, "r": 558.20178, "b": 442.89386, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 444.37823, "r": 429.42697, "b": 450.86386, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": ", ICDAR, pages 605-617. LNCS 12824, Springer-", "bbox": {"l": 429.42400999999995, "t": 444.34335, "r": 559.37872, "b": 450.85689999999994, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Verlag, sep 2021.", "bbox": {"l": 332.819, "t": 452.31335, "r": 380.01764, "b": 458.8269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[4] Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on scientific literature parsing. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 605-617. LNCS 12824, SpringerVerlag, sep 2021."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "list_item", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 321.198, "t": 460.28336, "r": 559.02625, "b": 490.70792, "coord_origin": "TOPLEFT"}, "confidence": 0.9289959073066711, "cells": [{"id": 159, "text": "[5]", "bbox": {"l": 321.198, "t": 460.28336, "r": 329.22977, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin,", "bbox": {"l": 330.93576, "t": 460.28336, "r": 558.97156, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis:", "bbox": {"l": 333.39099, "t": 468.25336, "r": 559.02625, "b": 474.76691, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "not dead yet.", "bbox": {"l": 333.39099, "t": 476.22437, "r": 368.85431, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "International Journal on Document Analysis and Recognition (IJDAR)", "bbox": {"l": 370.811, "t": 476.25925, "r": 557.46326, "b": 482.74487, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": ",", "bbox": {"l": 557.46503, "t": 476.22437, "r": 558.96857, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "pages 1-11, 01 2022.", "bbox": {"l": 333.39099, "t": 484.19437, "r": 390.82715, "b": 490.70792, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[5] Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin, Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis: not dead yet. 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In", "bbox": {"l": 333.39099, "t": 500.13437, "r": 438.6101100000001, "b": 506.64792, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "Proceedings of the International Conference", "bbox": {"l": 440.349, "t": 500.16925, "r": 558.19958, "b": 506.65488, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "on Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 508.13925, "r": 441.40118, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": ", ICDAR, pages 1015-1022, sep 2019.", "bbox": {"l": 441.4019799999999, "t": 508.10437, "r": 544.78162, "b": 514.61792, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[6] Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset ever for document layout analysis. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 1015-1022, sep 2019."}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 321.19797, "t": 516.07437, "r": 558.9715, "b": 554.46889, "coord_origin": "TOPLEFT"}, "confidence": 0.9426887035369873, "cells": [{"id": 172, "text": "[7]", "bbox": {"l": 321.19797, "t": 516.07437, "r": 329.74161, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and", "bbox": {"l": 331.55634, "t": 516.07437, "r": 558.19897, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In", "bbox": {"l": 333.39099, "t": 524.0443700000001, "r": 558.19891, "b": 530.55792, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "Proceedings of the 28th International Conference on Computational Linguistics", "bbox": {"l": 333.39099, "t": 532.04922, "r": 557.40228, "b": 538.53487, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": ",", "bbox": {"l": 557.40399, "t": 532.01437, "r": 558.96893, "b": 538.5278900000001, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "COLING, pages 949-960. International Committee on Computational Linguistics,", "bbox": {"l": 333.39099, "t": 539.98438, "r": 558.9715, "b": 546.49789, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "dec 2020.", "bbox": {"l": 333.39099, "t": 547.95535, "r": 359.31955, "b": 554.46889, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[7] Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and Ming Zhou. Docbank: A benchmark dataset for document layout analysis. 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In", "bbox": {"l": 333.39099, "t": 563.89536, "r": 535.54352, "b": 570.40889, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "SemWe-", "bbox": {"l": 536.96399, "t": 563.93024, "r": 558.90222, "b": 570.4158600000001, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "bEval@ESWC", "bbox": {"l": 333.39099, "t": 571.9002399999999, "r": 371.94217, "b": 578.38586, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": ", 2016.", "bbox": {"l": 371.94299, "t": 571.86536, "r": 389.72617, "b": 578.3788900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[8] Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction from pdf sources based on rule-based system using integrated formats. 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In", "bbox": {"l": 333.39099, "t": 587.8053600000001, "r": 543.05487, "b": 594.31889, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "IEEE", "bbox": {"l": 544.98499, "t": 587.84024, "r": 558.20148, "b": 594.32587, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Conference on Computer Vision and Pattern Recognition", "bbox": {"l": 333.39099, "t": 595.81024, "r": 491.61166, "b": 602.29587, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": ", CVPR, pages 580-587.", "bbox": {"l": 491.61301, "t": 595.77536, "r": 559.27448, "b": 602.28889, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "IEEE Computer Society, jun 2014.", "bbox": {"l": 333.39099, "t": 603.74536, "r": 428.59726000000006, "b": 610.2589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[9] Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. 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IEEE Computer Society, dec 2015."}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859862327575684, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "list_item", "id": 20, "page_no": 7, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 317.95499, "t": 627.65637, "r": 558.20142, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9127580523490906, "cells": [{"id": 197, "text": "[11]", "bbox": {"l": 317.95499, "t": 627.65637, "r": 329.5459, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 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Nassar, and Peter Staar"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "4bed2a8aa51ac37058e79605821bbc426d032b0b6ca8bdf3409ed8508ccd8c67", "bbox": {"l": 231.8804, "t": 301.50543, "r": 235.14504999999997, "b": 414.69144, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "2f2a06d08f5ad565d0f5e815f4ddf666365b2cff435cdaeb8850217e8a8efabf", "bbox": {"l": 395.06876, "t": 117.37183000000005, "r": 398.33353, "b": 230.55786, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "7f2fd7293e04bf4f1756ae51f5779764933da1d1d2002e3915356050570fc75b", "bbox": {"l": 55.775887, "t": 301.50543, "r": 59.04052000000001, "b": 414.69144, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "1b81cf65f47456ad4faa725d1eb09879bd633af16cfe2bf8cea661b87907bfac", "bbox": {"l": 232.01364, "t": 117.37183000000005, "r": 235.27841000000004, "b": 230.55786, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "b60da9d26f488cb133e47d101d35fda1bdca2671ade60764d1cd569590270327", "bbox": {"l": 395.20047, "t": 301.50543, "r": 398.46512, "b": 414.69144, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "2b7b8355a42ebef0cf91583aad9f30f7c9fa63c5b05911730ba15275c024965b$^{A}$", "bbox": {"l": 55.775818, "t": 117.37183000000005, "r": 65.409912, "b": 230.55786, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "B", "bbox": {"l": 234.56980999999996, "t": 88.50183000000015, "r": 240.06987, "b": 97.01098999999988, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 397.81934, "t": 88.89355, "r": 403.3194, "b": 97.40270999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "D", "bbox": {"l": 59.909843, "t": 266.75885000000005, "r": 65.409912, "b": 275.26793999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "E", "bbox": {"l": 234.77386, "t": 266.36707, "r": 239.85495000000003, "b": 274.87616, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "F", "bbox": {"l": 398.26144, "t": 266.75885000000005, "r": 402.91592, "b": 275.26793999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Text", "bbox": {"l": 62.323874999999994, "t": 442.28543, "r": 70.895882, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Caption", "bbox": {"l": 80.16581, "t": 442.28543, "r": 95.565453, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "List-Item", "bbox": {"l": 104.94447, "t": 442.28543, "r": 122.38113000000001, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Formula", "bbox": {"l": 131.78354, "t": 442.28543, "r": 148.34625, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Table", "bbox": {"l": 157.66106, "t": 442.28543, "r": 168.53032, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Section-Header", "bbox": {"l": 201.24315, "t": 442.28543, "r": 232.00499, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Picture", "bbox": {"l": 177.8381, "t": 442.28543, "r": 191.88956, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Page-Header", "bbox": {"l": 240.95844000000002, "t": 442.28543, "r": 266.61908, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Page-Footer", "bbox": {"l": 276.03928, "t": 442.28543, "r": 300.33261, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Title", "bbox": {"l": 309.74615, "t": 442.28543, "r": 318.50473, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on", "bbox": {"l": 53.79800000000001, "t": 464.48199, "r": 558.203, "b": 472.95523, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demon-", "bbox": {"l": 53.79800000000001, "t": 475.44101, "r": 559.80786, "b": 483.91425, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "strates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion", "bbox": {"l": 53.79800000000001, "t": 486.39999, "r": 558.20294, "b": 494.87323, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "and missing boxes.", "bbox": {"l": 53.79800000000001, "t": 497.358, "r": 130.37105, "b": 505.83124, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ul-", "bbox": {"l": 69.234001, "t": 527.06635, "r": 295.22406, "b": 533.5799, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow", "bbox": {"l": 69.234001, "t": 535.03638, "r": 294.30612, "b": 541.5499, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "export, opencv dnn support, October 2021.", "bbox": {"l": 69.234001, "t": 543.00638, "r": 190.45259, "b": 549.5199, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "[14]", "bbox": {"l": 53.79800000000001, "t": 550.97638, "r": 65.286942, "b": 557.4899, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander", "bbox": {"l": 67.036171, "t": 550.97638, "r": 294.17709, "b": 557.4899, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Kirillov, and Sergey Zagoruyko. 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Asso-", "bbox": {"l": 144.908, "t": 694.4383700000001, "r": 295.22174, "b": 700.951897, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ciation for Computing Machinery.", "bbox": {"l": 69.234001, "t": 702.408363, "r": 166.37207, "b": 708.92189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 1192-1200, New York, USA, 2020. Association for Computing Machinery."}], "headers": [{"label": "page_header", "id": 13, "page_no": 8, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8021655082702637, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8429455161094666, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}]
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Nassar", "bbox": {"l": 182.26804, "t": 192.05737, "r": 265.39255, "b": 203.22357, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "IBM Research", "bbox": {"l": 195.87103, "t": 205.40923999999995, "r": 251.98038999999997, "b": 214.71429, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Rueschlikon, Switzerland", "bbox": {"l": 172.54303, "t": 217.36425999999994, "r": 275.30725, "b": 226.66931, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "ahn@zurich.ibm.com", "bbox": {"l": 180.52803, "t": 229.32025, "r": 267.3222, "b": 238.62531, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Peter Staar", "bbox": {"l": 361.52802, "t": 192.05737, "r": 414.84821, "b": 203.22357, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "IBM Research", "bbox": {"l": 360.02002, "t": 205.40923999999995, "r": 416.12939, "b": 214.71429, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Rueschlikon, Switzerland", "bbox": {"l": 336.69302, "t": 217.36425999999994, "r": 439.45727999999997, "b": 226.66931, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "taa@zurich.ibm.com", "bbox": {"l": 346.20703, "t": 229.32025, "r": 429.94269, "b": 238.62531, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "ABSTRACT", "bbox": {"l": 53.798035, "t": 247.70288000000005, "r": 111.94354, "b": 258.01202, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Accurate document layout analysis is a key requirement for high-", "bbox": {"l": 53.484001, "t": 262.90454, "r": 295.55591, "b": 271.27917, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "quality PDF document conversion. With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "13", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "USING THE VERTICAL TUBE -", "bbox": {"l": 327.83005, "t": 331.57268999999997, "r": 351.16092, "b": 333.31171, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "MODELS AY11230/11234", "bbox": {"l": 327.83005, "t": 333.18292, "r": 348.30536, "b": 334.92194, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, 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The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 239, "label": "text", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 240, "label": "text", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 295, "text": "the landing surface. 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "caption", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 559.51524, "coord_origin": "TOPLEFT"}, "confidence": 0.7793391346931458, "cells": [{"id": 409, "text": "Figure 1: Four examples of complex page layouts across dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458844423294067, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; 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While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "caption", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 559.51524, "coord_origin": "TOPLEFT"}, "confidence": 0.7793391346931458, "cells": [{"id": 409, "text": "Figure 1: Four examples of complex page layouts across dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Four examples of complex page layouts across different document categories"}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458844423294067, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; 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Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. 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Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Washington, DC, USA. 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "caption", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 559.51524, "coord_origin": "TOPLEFT"}, "confidence": 0.7793391346931458, "cells": [{"id": 409, "text": "Figure 1: Four examples of complex page layouts across dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Four examples of complex page layouts across different document categories"}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458844423294067, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; 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\u2022 Applied computing \u2192 Document analysis ; \u2022 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9208475351333618, "cells": [{"id": 411, "text": "KEYWORDS", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KEYWORDS"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 627.00117, "coord_origin": "TOPLEFT"}, "confidence": 0.9509093761444092, "cells": [{"id": 412, "text": "PDF document conversion, layout segmentation, object-detection,", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 616.04218, "coord_origin": "TOPLEFT"}}, {"id": 413, "text": "data set, Machine Learning", "bbox": {"l": 317.95499, "t": 618.62656, "r": 416.94403, "b": 627.00117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning"}, {"label": "text", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "text", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 295.11798, "b": 672.79189, "coord_origin": "TOPLEFT"}, "confidence": 0.7107337117195129, "cells": [{"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.65997, "t": 640.05434, "r": 404.65366, "b": 647.58609, "coord_origin": "TOPLEFT"}, "confidence": 0.8721982836723328, "cells": [{"id": 414, "text": "ACM Reference Format:", "bbox": {"l": 317.65997, "t": 640.05434, "r": 404.65366, "b": 647.58609, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACM Reference Format:"}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 650.11996, "r": 559.5495, "b": 707.377029, "coord_origin": "TOPLEFT"}, "confidence": 0.9455163478851318, "cells": [{"id": 415, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter", "bbox": {"l": 317.95499, "t": 650.11996, "r": 558.35266, "b": 657.56404, "coord_origin": "TOPLEFT"}}, {"id": 416, "text": "Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for Document-", "bbox": {"l": 317.95499, "t": 660.08296, "r": 559.5495, "b": 667.52703, "coord_origin": "TOPLEFT"}}, {"id": 417, "text": "Layout Analysis. In", "bbox": {"l": 317.95499, "t": 670.04497, "r": 383.30807, "b": 677.48904, "coord_origin": "TOPLEFT"}}, {"id": 418, "text": "Proceedings of the 28th ACM SIGKDD Conference on", "bbox": {"l": 385.798, "t": 670.08482, "r": 558.20032, "b": 677.49701, "coord_origin": "TOPLEFT"}}, {"id": 419, "text": "Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Wash-", "bbox": {"l": 317.95499, "t": 680.04781, "r": 559.00092, "b": 687.46001, "coord_origin": "TOPLEFT"}}, {"id": 420, "text": "ington, DC, USA.", "bbox": {"l": 317.95499, "t": 690.01081, "r": 370.11481, "b": 697.423004, "coord_origin": "TOPLEFT"}}, {"id": 421, "text": "ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/", "bbox": {"l": 371.82999, "t": 689.97096, "r": 558.71655, "b": 697.415031, "coord_origin": "TOPLEFT"}}, {"id": 422, "text": "3534678.3539043", "bbox": {"l": 317.95499, "t": 699.932953, "r": 371.59375, "b": 707.377029, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Washington, DC, USA. 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Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680323123931885, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367701411247253, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996682524681091, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279747962952, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813894629478455, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844210147857666, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792873859405518, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347410202026367, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795214533805847, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330971837043762, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019714355469, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594184160232544, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615143537521362, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447574615478516, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749842882156372, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586857914924622, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680323123931885, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367701411247253, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996682524681091, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279747962952, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813894629478455, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844210147857666, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792873859405518, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347410202026367, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795214533805847, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330971837043762, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019714355469, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594184160232544, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615143537521362, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447574615478516, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749842882156372, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586857914924622, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}], "body": [{"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367701411247253, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996682524681091, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279747962952, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813894629478455, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844210147857666, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792873859405518, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347410202026367, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795214533805847, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330971837043762, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019714355469, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594184160232544, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615143537521362, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447574615478516, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749842882156372, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586857914924622, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}], "headers": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680323123931885, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920252323150635, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.91046142578125, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "picture", "bbox": {"l": 88.32998657226562, "t": 92.88650512695312, "r": 263.70513916015625, "b": 220.56797790527344, "coord_origin": "TOPLEFT"}, "confidence": 0.9698249101638794, "cells": [], "children": [{"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425569772720337, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048321843147278, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920252323150635, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.91046142578125, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.32998657226562, "t": 92.88650512695312, "r": 263.70513916015625, "b": 220.56797790527344, "coord_origin": "TOPLEFT"}, "confidence": 0.9698249101638794, "cells": [], "children": [{"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 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"TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "caption", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425569772720337, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \u201cinvisible\u201d tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \u201cinvisible\u201d list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \u201ctext in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048321843147278, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}], "body": [{"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.32998657226562, "t": 92.88650512695312, "r": 263.70513916015625, "b": 220.56797790527344, "coord_origin": "TOPLEFT"}, "confidence": 0.9698249101638794, "cells": [], "children": [{"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "caption", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425569772720337, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \u201cinvisible\u201d tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \u201cinvisible\u201d list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \u201ctext in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048321843147278, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}], "headers": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920252323150635, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.91046142578125, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 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"coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 418.16003, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "95-99", "bbox": {"l": 428.1217, "t": 162.53954999999996, "r": 447.82986, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "69-78", "bbox": {"l": 457.80051, "t": 162.53954999999996, "r": 477.50867, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "n/a", "bbox": {"l": 495.32489, "t": 162.53954999999996, "r": 507.17846999999995, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Footnote", "bbox": {"l": 104.825, "t": 173.49854000000005, "r": 137.3282, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "6318", "bbox": {"l": 182.035, "t": 173.49854000000005, "r": 198.71251, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "0.60", "bbox": {"l": 219.211, "t": 173.49854000000005, "r": 233.69174000000004, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "0.31", "bbox": {"l": 250.01956, "t": 173.49854000000005, "r": 264.50031, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "0.58", "bbox": {"l": 280.82812, "t": 173.49854000000005, "r": 295.30887, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "83-91", "bbox": {"l": 305.27301, "t": 173.49854000000005, "r": 324.98117, "b": 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"coord_origin": "TOPLEFT"}}, {"id": 162, "text": "68-85", "bbox": {"l": 487.47034, "t": 283.48654, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625211477279663, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906538486480713, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 98.93107604980469, "t": 137.4754638671875, "r": 512.5799560546875, "b": 294.08154296875, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 418.16003, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "95-99", "bbox": {"l": 428.1217, "t": 162.53954999999996, "r": 447.82986, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "69-78", "bbox": {"l": 457.80051, "t": 162.53954999999996, "r": 477.50867, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "n/a", "bbox": {"l": 495.32489, "t": 162.53954999999996, "r": 507.17846999999995, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Footnote", "bbox": {"l": 104.825, "t": 173.49854000000005, "r": 137.3282, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "6318", "bbox": {"l": 182.035, "t": 173.49854000000005, "r": 198.71251, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "0.60", "bbox": {"l": 219.211, "t": 173.49854000000005, "r": 233.69174000000004, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "0.31", "bbox": {"l": 250.01956, "t": 173.49854000000005, "r": 264.50031, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "0.58", "bbox": {"l": 280.82812, "t": 173.49854000000005, "r": 295.30887, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "83-91", "bbox": {"l": 305.27301, "t": 173.49854000000005, "r": 324.98117, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "n/a", "bbox": {"l": 342.79739, "t": 173.49854000000005, "r": 354.65097, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "100", "bbox": {"l": 371.81265, "t": 173.49854000000005, "r": 384.32077, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "62-88", "bbox": {"l": 398.45181, "t": 173.49854000000005, "r": 418.15997, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "85-94", "bbox": {"l": 428.12164, "t": 173.49854000000005, "r": 447.8298, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "n/a", "bbox": {"l": 465.655, "t": 173.49854000000005, "r": 477.50857999999994, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "82-97", "bbox": {"l": 487.47025, "t": 173.49854000000005, "r": 507.17841, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Formula", "bbox": {"l": 104.825, "t": 184.45752000000005, "r": 135.33766, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "25027", "bbox": {"l": 177.866, "t": 184.45752000000005, "r": 198.71288, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "2.25", "bbox": {"l": 219.211, "t": 184.45752000000005, "r": 233.69174000000004, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1.90", "bbox": {"l": 250.01956, "t": 184.45752000000005, "r": 264.50031, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "2.96", "bbox": {"l": 280.82812, "t": 184.45752000000005, "r": 295.30887, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "83-85", "bbox": {"l": 305.27301, "t": 184.45752000000005, "r": 324.98117, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "n/a", "bbox": {"l": 342.79739, "t": 184.45752000000005, "r": 354.65097, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "n/a", "bbox": {"l": 372.46719, "t": 184.45752000000005, "r": 384.32077, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "84-87", "bbox": {"l": 398.45181, "t": 184.45752000000005, "r": 418.15997, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "86-96", "bbox": {"l": 428.12164, "t": 184.45752000000005, "r": 447.8298, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "n/a", "bbox": {"l": 465.655, "t": 184.45752000000005, "r": 477.50857999999994, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "n/a", "bbox": {"l": 495.3248, "t": 184.45752000000005, "r": 507.17838000000006, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "List-item", "bbox": {"l": 104.825, 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Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847549200057983, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64874, "b": 606.31924, "coord_origin": "TOPLEFT"}, "confidence": 0.8104852437973022, "cells": [{"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.98039311170578, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.974835991859436, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"1": {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93107604980469, "t": 137.4754638671875, "r": 512.5799560546875, "b": 294.08154296875, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 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"assembled": {"elements": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625211477279663, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906538486480713, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93107604980469, "t": 137.4754638671875, "r": 512.5799560546875, "b": 294.08154296875, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, 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"predicted_class": null, "confidence": null}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818442463874817, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847549200057983, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64874, "b": 606.31924, "coord_origin": "TOPLEFT"}, "confidence": 0.8104852437973022, "cells": [{"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.98039311170578, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.974835991859436, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{3}$https://arxiv.org/"}], "body": [{"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906538486480713, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93107604980469, "t": 137.4754638671875, "r": 512.5799560546875, "b": 294.08154296875, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, 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"predicted_class": null, "confidence": null}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818442463874817, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847549200057983, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64874, "b": 606.31924, "coord_origin": "TOPLEFT"}, "confidence": 0.8104852437973022, "cells": [{"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.98039311170578, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.974835991859436, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{3}$https://arxiv.org/"}], "headers": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625211477279663, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.887782871723175, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534672856330872, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264117002487183, "cells": [], "children": [{"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, 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"the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691525101661682, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873710632324219, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544305205345154, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611717462539673, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.982954204082489, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.887782871723175, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534672856330872, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264117002487183, "cells": [], "children": [{"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 84, "text": 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326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.902275800704956, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691525101661682, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "text", "id": 22, "page_no": 4, "cluster": {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"label": "caption", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873710632324219, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544305205345154, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611717462539673, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.982954204082489, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other\u2019s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}], "body": [{"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264117002487183, "cells": [], "children": [{"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.902275800704956, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691525101661682, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "text", "id": 22, "page_no": 4, "cluster": {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"label": "caption", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873710632324219, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544305205345154, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611717462539673, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.982954204082489, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other\u2019s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}], "headers": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.887782871723175, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534672856330872, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "human", "bbox": {"l": 132.36501, "t": 197.97351000000003, "r": 157.99098, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "MRCNN", "bbox": {"l": 173.505, "t": 197.97351000000003, "r": 204.61841, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "FRCNN", "bbox": {"l": 220.13028, "t": 197.97351000000003, "r": 248.06958, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "YOLO", "bbox": {"l": 258.03125, "t": 197.97351000000003, "r": 280.17825, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "R50", "bbox": {"l": 168.39301, "t": 208.93255999999997, "r": 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{"id": 68, "text": "74.6", "bbox": {"l": 261.86804, "t": 297.00253, "r": 276.34879, "b": 305.37717, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Table", "bbox": {"l": 67.663002, "t": 307.96155, "r": 87.46978, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "77-81", "bbox": {"l": 135.32401, "t": 307.96155, "r": 155.03215, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "82.2", "bbox": {"l": 167.95399, "t": 307.96155, "r": 182.43472, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "82.9", "bbox": {"l": 194.0462, "t": 307.96155, "r": 208.52695, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "82.2", "bbox": {"l": 226.86324000000002, "t": 307.96155, "r": 241.34396, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "86.3", "bbox": {"l": 261.86804, "t": 307.96155, "r": 276.34879, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Text", "bbox": {"l": 67.663002, "t": 318.91953, "r": 83.623199, "b": 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167.95399, "t": 329.87854, "r": 182.43472, "b": 338.25317, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "80.4", "bbox": {"l": 194.0462, "t": 329.87854, "r": 208.52695, "b": 338.25317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "79.9", "bbox": {"l": 226.86324000000002, "t": 329.87854, "r": 241.34396, "b": 338.25317, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "82.7", "bbox": {"l": 261.86804, "t": 329.87854, "r": 276.34879, "b": 338.25317, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "All", "bbox": {"l": 67.663002, "t": 341.23654, "r": 78.628906, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "82-83", "bbox": {"l": 135.32401, "t": 341.23654, "r": 155.03215, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "72.4", "bbox": {"l": 167.95399, "t": 341.23654, "r": 182.43472, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "73.5", "bbox": {"l": 194.0462, "t": 341.23654, "r": 208.52695, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "73.4", "bbox": {"l": 226.86324000000002, "t": 341.23654, "r": 241.34396, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "76.8", "bbox": {"l": 261.86804, "t": 341.23654, "r": 276.34879, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "0", "bbox": {"l": 349.16577, "t": 246.68017999999995, "r": 352.48175, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "20", "bbox": {"l": 385.93698, "t": 246.68017999999995, "r": 392.56894, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "40", "bbox": {"l": 424.366, "t": 246.68017999999995, "r": 430.99796, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "60", "bbox": {"l": 462.79504000000003, "t": 246.68017999999995, "r": 469.427, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "80", "bbox": {"l": 501.22406, "t": 246.68017999999995, "r": 507.85602, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "100", "bbox": {"l": 537.99524, "t": 246.68017999999995, "r": 547.94318, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "% of DocLayNet training set", "bbox": {"l": 410.28143, "t": 253.80840999999998, "r": 483.47278000000006, "b": 259.88251, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "50", "bbox": {"l": 330.93539, "t": 218.38464, "r": 337.56735, "b": 224.45874000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "55", "bbox": {"l": 330.93539, "t": 192.08660999999995, "r": 337.56735, "b": 198.16071, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "60", "bbox": {"l": 330.93539, "t": 165.78864, "r": 337.56735, "b": 171.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "65", "bbox": {"l": 330.93539, "t": 139.49059999999997, "r": 337.56735, "b": 145.56470000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "70", "bbox": {"l": 330.93539, "t": 113.19263000000001, "r": 337.56735, "b": 119.26671999999996, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "mAP 0.50:0.95", "bbox": {"l": 322.92276, "t": 148.37689, "r": 328.99686, "b": 186.79218000000003, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "10", "bbox": {"l": 470.97235, "t": 235.36676, "r": 477.6055, "b": 241.44086000000004, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "1", "bbox": {"l": 477.65662, "t": 234.82390999999996, "r": 479.97778000000005, "b": 239.07581000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "10", "bbox": {"l": 531.55127, "t": 235.41234999999995, "r": 538.18445, "b": 241.48645, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "2", "bbox": {"l": 538.23553, "t": 234.86951, "r": 540.5567, "b": 239.1214, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "50", "bbox": {"l": 404.91125, "t": 216.00005999999996, "r": 411.54321, "b": 222.07416, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "55", "bbox": {"l": 404.91125, "t": 200.22125000000005, "r": 411.54321, "b": 206.29534999999998, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "60", "bbox": {"l": 404.91125, "t": 184.44244000000003, "r": 411.54321, "b": 190.51653999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "65", "bbox": {"l": 404.91125, "t": 168.66364, "r": 411.54321, "b": 174.73773000000006, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "70", "bbox": {"l": 404.91125, "t": 152.88489000000004, "r": 411.54321, "b": 158.95898, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. 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The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.528999, "t": 370.92755, "r": 295.55612, "b": 576.56317, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579004049301147, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.9876052141189575, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "table", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "table", "bbox": {"l": 62.02751922607422, "t": 195.68003845214844, "r": 285.78955078125, "b": 351.6618957519531, "coord_origin": "TOPLEFT"}, 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The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.528999, "t": 370.92755, "r": 295.55612, "b": 576.56317, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579004049301147, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.9876052141189575, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}], "body": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64874, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9782734513282776, "cells": [{"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. 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"end_col_offset_idx": 6, "text": "76.8", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 317.95499, "t": 279.01599, "r": 559.80579, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.963992178440094, "cells": [{"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.528999, "t": 370.92755, "r": 295.55612, "b": 576.56317, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579004049301147, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.9876052141189575, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}], "headers": [{"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8662774562835693, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. 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"b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "72", "bbox": {"l": 151.07401, "t": 285.40454, "r": 159.41275, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "73", "bbox": {"l": 177.23795, "t": 285.40454, "r": 185.57669, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "78", "bbox": {"l": 211.25645, "t": 285.40454, "r": 219.5952, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "77", "bbox": {"l": 245.27496, "t": 285.40454, "r": 253.61371, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Class-count", "bbox": {"l": 358.63901, "t": 153.10051999999996, "r": 401.73154, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "11", "bbox": {"l": 440.22501, "t": 153.10051999999996, "r": 448.56375, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "5", "bbox": {"l": 494.38, "t": 153.10051999999996, "r": 498.54938, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Split", "bbox": {"l": 358.63901, "t": 164.05951000000005, "r": 375.27167, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Doc", "bbox": {"l": 423.341, "t": 164.05951000000005, "r": 438.0459, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Page", "bbox": {"l": 448.00757, "t": 164.05951000000005, "r": 465.4472, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Doc", "bbox": {"l": 475.41101, "t": 164.05951000000005, "r": 490.11591, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Page", "bbox": {"l": 500.07757999999995, "t": 164.05951000000005, "r": 517.51721, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Caption", "bbox": {"l": 358.63901, "t": 175.41656, "r": 387.82465, "b": 183.7912, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "68", "bbox": {"l": 426.52399, "t": 175.41656, "r": 434.86273, "b": 183.7912, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "83", "bbox": {"l": 452.56240999999994, "t": 175.41656, "r": 460.90115000000003, "b": 183.7912, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Footnote", "bbox": {"l": 358.63901, "t": 186.37554999999998, "r": 391.14221, "b": 194.75018, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "71", 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"coord_origin": "TOPLEFT"}}, {"id": 168, "text": "84", "bbox": {"l": 452.56240999999994, "t": 296.36255, "r": 460.90115000000003, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "78", "bbox": {"l": 478.59399, "t": 296.36255, "r": 486.93274, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "87", "bbox": {"l": 504.6324200000001, "t": 296.36255, "r": 512.97119, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9316117763519287, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.8296932578086853, "cells": [{"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.87362140417099, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 352.97747802734375, "t": 150.79122924804688, "r": 522.9158935546875, "b": 306.265869140625, "coord_origin": "TOPLEFT"}, "confidence": 0.9879695177078247, "cells": [{"id": 115, "text": "Class-count", "bbox": {"l": 358.63901, "t": 153.10051999999996, "r": 401.73154, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "11", "bbox": {"l": 440.22501, "t": 153.10051999999996, "r": 448.56375, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "5", "bbox": {"l": 494.38, "t": 153.10051999999996, "r": 498.54938, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Split", "bbox": {"l": 358.63901, "t": 164.05951000000005, "r": 375.27167, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Doc", "bbox": {"l": 423.341, "t": 164.05951000000005, "r": 438.0459, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Page", "bbox": {"l": 448.00757, "t": 164.05951000000005, "r": 465.4472, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Doc", "bbox": {"l": 475.41101, "t": 164.05951000000005, "r": 490.11591, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Page", "bbox": {"l": 500.07757999999995, "t": 164.05951000000005, "r": 517.51721, "b": 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{"l": 504.6324200000001, "t": 208.29351999999994, "r": 512.97119, "b": 216.66814999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 358.63901, "t": 219.25256000000002, "r": 401.16666, "b": 227.62720000000002, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "62", "bbox": {"l": 426.52399, "t": 219.25256000000002, "r": 434.86273, "b": 227.62720000000002, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "89", "bbox": {"l": 452.56240999999994, "t": 219.25256000000002, "r": 460.90115000000003, "b": 227.62720000000002, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Page-header", "bbox": {"l": 358.63901, "t": 230.21155, "r": 403.91931, "b": 238.58618, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "72", "bbox": {"l": 426.52399, "t": 230.21155, "r": 434.86273, "b": 238.58618, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "90", "bbox": {"l": 452.56240999999994, "t": 230.21155, "r": 460.90115000000003, "b": 238.58618, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Picture", "bbox": {"l": 358.63901, "t": 241.16956000000005, "r": 384.62366, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "72", "bbox": {"l": 426.52399, "t": 241.16956000000005, "r": 434.86273, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "82", "bbox": {"l": 452.56240999999994, "t": 241.16956000000005, "r": 460.90115000000003, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "72", "bbox": {"l": 478.59399, "t": 241.16956000000005, "r": 486.93274, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "82", "bbox": {"l": 504.6324200000001, "t": 241.16956000000005, "r": 512.97119, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Section-header", "bbox": {"l": 358.63901, "t": 252.12854000000004, "r": 413.37891, "b": 260.50316999999995, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "68", "bbox": {"l": 426.52399, "t": 252.12854000000004, "r": 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The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572024941444397, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589216113090515, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 352.97747802734375, "t": 150.79122924804688, "r": 522.9158935546875, "b": 306.265869140625, "coord_origin": "TOPLEFT"}, "confidence": 0.9879695177078247, "cells": [{"id": 115, "text": "Class-count", "bbox": {"l": 358.63901, "t": 153.10051999999996, "r": 401.73154, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "11", "bbox": {"l": 440.22501, "t": 153.10051999999996, "r": 448.56375, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "5", "bbox": {"l": 494.38, "t": 153.10051999999996, "r": 498.54938, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Split", "bbox": {"l": 358.63901, "t": 164.05951000000005, "r": 375.27167, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Doc", "bbox": {"l": 423.341, "t": 164.05951000000005, "r": 438.0459, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Page", "bbox": {"l": 448.00757, "t": 164.05951000000005, "r": 465.4472, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Doc", "bbox": {"l": 475.41101, "t": 164.05951000000005, "r": 490.11591, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Page", "bbox": {"l": 500.07757999999995, "t": 164.05951000000005, "r": 517.51721, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Caption", "bbox": {"l": 358.63901, "t": 175.41656, "r": 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0.9555687308311462, "cells": [{"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Learning Curve"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740145802497864, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "text", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is \u201clarge enough\u201d. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572024941444397, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589216113090515, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "body": [{"label": "text", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.8296932578086853, "cells": [{"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels."}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.87362140417099, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 352.97747802734375, "t": 150.79122924804688, "r": 522.9158935546875, "b": 306.265869140625, "coord_origin": "TOPLEFT"}, "confidence": 0.9879695177078247, "cells": [{"id": 115, "text": "Class-count", "bbox": {"l": 358.63901, "t": 153.10051999999996, "r": 401.73154, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "11", "bbox": {"l": 440.22501, "t": 153.10051999999996, "r": 448.56375, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "5", "bbox": {"l": 494.38, "t": 153.10051999999996, "r": 498.54938, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Split", "bbox": {"l": 358.63901, "t": 164.05951000000005, "r": 375.27167, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Doc", "bbox": {"l": 423.341, "t": 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0.9555687308311462, "cells": [{"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Learning Curve"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740145802497864, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "text", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is \u201clarge enough\u201d. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572024941444397, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589216113090515, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "headers": [{"label": "page_header", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9316117763519287, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.32153, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 295.64868, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "cLayNet data-sets. By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Testing on", "bbox": {"l": 217.74099999999999, "t": 175.01855, "r": 256.26065, "b": 183.39319, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Training on", "bbox": {"l": 89.954002, "t": 185.97655999999995, "r": 133.24379, "b": 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{"id": 66, "text": "59", "bbox": {"l": 208.44701, "t": 329.63855, "r": 216.78575000000004, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "47", "bbox": {"l": 232.1183, "t": 329.63855, "r": 240.45705, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "78", "bbox": {"l": 256.49792, "t": 329.63855, "r": 264.83667, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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In", "bbox": {"l": 333.39099, "t": 563.89536, "r": 535.54352, "b": 570.40889, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "SemWe-", "bbox": {"l": 536.96399, "t": 563.93024, "r": 558.90222, "b": 570.4158600000001, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "bEval@ESWC", "bbox": {"l": 333.39099, "t": 571.9002399999999, "r": 371.94217, "b": 578.38586, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": ", 2016.", "bbox": {"l": 371.94299, "t": 571.86536, "r": 389.72617, "b": 578.3788900000001, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "[9]", "bbox": {"l": 321.198, "t": 579.83536, "r": 329.45999, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature", "bbox": {"l": 331.21487, "t": 579.83536, "r": 558.19843, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "hierarchies for accurate object detection and semantic segmentation. In", "bbox": {"l": 333.39099, "t": 587.8053600000001, "r": 543.05487, "b": 594.31889, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "IEEE", "bbox": {"l": 544.98499, "t": 587.84024, "r": 558.20148, "b": 594.32587, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Conference on Computer Vision and Pattern Recognition", "bbox": {"l": 333.39099, "t": 595.81024, "r": 491.61166, "b": 602.29587, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": ", CVPR, pages 580-587.", "bbox": {"l": 491.61301, "t": 595.77536, "r": 559.27448, "b": 602.28889, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "IEEE Computer Society, jun 2014.", "bbox": {"l": 333.39099, "t": 603.74536, "r": 428.59726000000006, "b": 610.2589, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "[10]", "bbox": {"l": 317.95499, "t": 611.71536, "r": 329.54767, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Ross B. Girshick. Fast R-CNN. In", "bbox": {"l": 331.31268, "t": 611.71536, "r": 425.1554, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "2015 IEEE International Conference on Computer", "bbox": {"l": 426.77802, "t": 611.75024, "r": 558.20203, "b": 618.23587, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Vision", "bbox": {"l": 333.39099, "t": 619.72124, "r": 350.59537, "b": 626.20686, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": ", ICCV, pages 1440-1448. IEEE Computer Society, dec 2015.", "bbox": {"l": 350.59598, "t": 619.68637, "r": 518.58777, "b": 626.19989, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "[11]", "bbox": {"l": 317.95499, "t": 627.65637, "r": 329.5459, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards", "bbox": {"l": 331.31064, "t": 627.65637, "r": 558.20142, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "real-time object detection with region proposal networks.", "bbox": {"l": 333.39099, "t": 635.62637, "r": 497.50909, "b": 642.13989, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "IEEE Transactions on", "bbox": {"l": 500.01401, "t": 635.66124, "r": 558.19885, "b": 642.14687, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Pattern Analysis and Machine Intelligence", "bbox": {"l": 333.39099, "t": 643.6312399999999, "r": 449.38620000000003, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": ", 39(6):1137-1149, 2017.", "bbox": {"l": 449.38699, "t": 643.59637, "r": 515.74268, "b": 650.10989, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "[12]", "bbox": {"l": 317.95499, "t": 651.56638, "r": 329.41763, "b": 658.0799, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross B. Girshick. Mask R-CNN.", "bbox": {"l": 331.16287, "t": 651.56638, "r": 559.27808, "b": 658.0799, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "In", "bbox": {"l": 333.39099, "t": 659.53638, "r": 339.35904, "b": 666.0499, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "IEEE International Conference on Computer Vision", "bbox": {"l": 341.56299, "t": 659.57124, "r": 485.8273, "b": 666.05687, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ", ICCV, pages 2980-2988.", "bbox": {"l": 485.82901, "t": 659.53638, "r": 559.27356, "b": 666.0499, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "IEEE Computer Society, Oct 2017.", "bbox": {"l": 333.39099, "t": 667.50636, "r": 429.30161000000004, "b": 674.01989, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "[13]", "bbox": {"l": 317.95499, "t": 675.47636, "r": 330.11407, "b": 681.9898900000001, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012,", "bbox": {"l": 331.96533, "t": 675.47636, "r": 558.96716, "b": 681.9898900000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "bbox": {"l": 333.18201, "t": 683.44637, "r": 558.96661, "b": 689.95989, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "bbox": {"l": 333.39099, "t": 691.41737, "r": 558.97156, "b": 697.930893, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "bbox": {"l": 333.39099, "t": 699.387367, "r": 558.20001, "b": 705.900894, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 24, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.79841548204422, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319990873336792, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64868, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9824119210243225, "cells": [{"id": 2, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.32153, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 295.64868, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "cLayNet data-sets. By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. 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Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 72.65901184082031, "t": 172.4807891845703, "r": 274.8346862792969, "b": 339.85400390625, "coord_origin": "TOPLEFT"}, "confidence": 0.9892617464065552, "cells": [{"id": 8, "text": "Testing on", "bbox": {"l": 217.74099999999999, "t": 175.01855, "r": 256.26065, "b": 183.39319, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Training on", "bbox": {"l": 89.954002, "t": 185.97655999999995, "r": 133.24379, "b": 194.35119999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "labels", "bbox": {"l": 154.629, "t": 185.97655999999995, "r": 175.47588, "b": 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other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9676095843315125, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table", "bbox": {"l": 331.53516, "t": 348.70233, "r": 558.19904, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "competition. In", "bbox": {"l": 333.39099, "t": 356.67236, "r": 379.36414, "b": 363.18591, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "2013 12th International Conference on Document Analysis and", "bbox": {"l": 381.37201, "t": 356.70724, "r": 558.20099, "b": 363.19287, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Recognition", "bbox": {"l": 333.39099, "t": 364.67724999999996, "r": 365.59601, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ", pages 1449-1453, 2013.", "bbox": {"l": 365.59601, "t": 364.64236, "r": 434.29489, "b": 371.15591, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 321.198, "t": 372.61237, "r": 559.37982, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9480941295623779, "cells": [{"id": 141, "text": "[2]", "bbox": {"l": 321.198, "t": 372.61237, "r": 329.85956, "b": 379.12592, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Ic-", "bbox": {"l": 331.69931, "t": 372.61237, "r": 559.37976, "b": 379.12592, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "dar2017 competition on recognition of documents with complex layouts -", "bbox": {"l": 333.39099, "t": 380.58237, "r": 559.37982, "b": 387.09592, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "rdcl2017. In", "bbox": {"l": 333.39099, "t": 388.55236999999994, "r": 367.4339, "b": 395.06592, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "2017 14th IAPR International Conference on Document Analysis and", "bbox": {"l": 369.17401, "t": 388.58725000000004, "r": 558.20422, "b": 395.07288, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Recognition (ICDAR)", "bbox": {"l": 333.39099, "t": 396.55725, "r": 390.87601, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": ", volume 01, pages 1404-1410, 2017.", "bbox": {"l": 390.87698, "t": 396.52237, "r": 492.17831, "b": 403.03592, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 390.92053, "r": 294.04712, "b": 443.14014, "coord_origin": "TOPLEFT"}, "confidence": 0.9839397668838501, "cells": [{"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 321.198, "t": 404.49237, "r": 558.20013, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9257484078407288, "cells": [{"id": 148, "text": "[3]", "bbox": {"l": 321.198, "t": 404.49237, "r": 329.53583, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian", "bbox": {"l": 331.30682, "t": 404.49237, "r": 558.19958, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and", "bbox": {"l": 333.39099, "t": 412.46335, "r": 558.20013, "b": 418.9769, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "Recognition (cTDaR), April 2019. http://sac.founderit.com/.", "bbox": {"l": 333.39099, "t": 420.43335, "r": 501.80127, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "list_item", "bbox": {"l": 321.198, "t": 428.40335, "r": 559.37872, "b": 458.8269, "coord_origin": "TOPLEFT"}, "confidence": 0.9270963668823242, "cells": [{"id": 152, "text": "[4]", "bbox": {"l": 321.198, "t": 428.40335, "r": 329.91299, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on", "bbox": {"l": 331.7641, "t": 428.40335, "r": 558.19885, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "scientific literature parsing. In", "bbox": {"l": 333.39099, "t": 436.3733500000001, "r": 423.77225, "b": 442.8869, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Proceedings of the International Conference on", "bbox": {"l": 425.909, "t": 436.40823, "r": 558.20178, "b": 442.89386, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 444.37823, "r": 429.42697, "b": 450.86386, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": ", ICDAR, pages 605-617. LNCS 12824, Springer-", "bbox": {"l": 429.42400999999995, "t": 444.34335, "r": 559.37872, "b": 450.85689999999994, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Verlag, sep 2021.", "bbox": {"l": 332.819, "t": 452.31335, "r": 380.01764, "b": 458.8269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "list_item", "bbox": {"l": 321.198, "t": 460.28336, "r": 559.02625, "b": 490.70792, "coord_origin": "TOPLEFT"}, "confidence": 0.9289959073066711, "cells": [{"id": 159, "text": "[5]", "bbox": {"l": 321.198, "t": 460.28336, "r": 329.22977, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin,", "bbox": {"l": 330.93576, "t": 460.28336, "r": 558.97156, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis:", "bbox": {"l": 333.39099, "t": 468.25336, "r": 559.02625, "b": 474.76691, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "not dead yet.", "bbox": {"l": 333.39099, "t": 476.22437, "r": 368.85431, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "International Journal on Document Analysis and Recognition (IJDAR)", "bbox": {"l": 370.811, "t": 476.25925, "r": 557.46326, "b": 482.74487, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": ",", "bbox": {"l": 557.46503, "t": 476.22437, "r": 558.96857, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "pages 1-11, 01 2022.", "bbox": {"l": 333.39099, "t": 484.19437, "r": 390.82715, "b": 490.70792, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "list_item", "bbox": {"l": 321.198, "t": 492.16437, "r": 558.20361, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.927467405796051, "cells": [{"id": 166, "text": "[6]", "bbox": {"l": 321.198, "t": 492.16437, "r": 329.42145, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset", "bbox": {"l": 331.16812, "t": 492.16437, "r": 558.20361, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "ever for document layout analysis. In", "bbox": {"l": 333.39099, "t": 500.13437, "r": 438.6101100000001, "b": 506.64792, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "Proceedings of the International Conference", "bbox": {"l": 440.349, "t": 500.16925, "r": 558.19958, "b": 506.65488, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "on Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 508.13925, "r": 441.40118, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": ", ICDAR, pages 1015-1022, sep 2019.", "bbox": {"l": 441.4019799999999, "t": 508.10437, "r": 544.78162, "b": 514.61792, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 321.19797, "t": 516.07437, "r": 558.9715, "b": 554.46889, "coord_origin": "TOPLEFT"}, "confidence": 0.9426887035369873, "cells": [{"id": 172, "text": "[7]", "bbox": {"l": 321.19797, "t": 516.07437, "r": 329.74161, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and", "bbox": {"l": 331.55634, "t": 516.07437, "r": 558.19897, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In", "bbox": {"l": 333.39099, "t": 524.0443700000001, "r": 558.19891, "b": 530.55792, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "Proceedings of the 28th International Conference on Computational Linguistics", "bbox": {"l": 333.39099, "t": 532.04922, "r": 557.40228, "b": 538.53487, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": ",", "bbox": {"l": 557.40399, "t": 532.01437, "r": 558.96893, "b": 538.5278900000001, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "COLING, pages 949-960. International Committee on Computational Linguistics,", "bbox": {"l": 333.39099, "t": 539.98438, "r": 558.9715, "b": 546.49789, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "dec 2020.", "bbox": {"l": 333.39099, "t": 547.95535, "r": 359.31955, "b": 554.46889, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "list_item", "bbox": {"l": 321.198, "t": 555.92535, "r": 558.90222, "b": 578.38586, "coord_origin": "TOPLEFT"}, "confidence": 0.9139776229858398, "cells": [{"id": 179, "text": "[8]", "bbox": {"l": 321.198, "t": 555.92535, "r": 329.7088, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction", "bbox": {"l": 331.51654, "t": 555.92535, "r": 558.19891, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "from pdf sources based on rule-based system using integrated formats. In", "bbox": {"l": 333.39099, "t": 563.89536, "r": 535.54352, "b": 570.40889, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "SemWe-", "bbox": {"l": 536.96399, "t": 563.93024, "r": 558.90222, "b": 570.4158600000001, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "bEval@ESWC", "bbox": {"l": 333.39099, "t": 571.9002399999999, "r": 371.94217, "b": 578.38586, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": ", 2016.", "bbox": {"l": 371.94299, "t": 571.86536, "r": 389.72617, "b": 578.3788900000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "list_item", "bbox": {"l": 321.198, "t": 579.83536, "r": 559.27448, "b": 610.2589, "coord_origin": "TOPLEFT"}, "confidence": 0.9113092422485352, "cells": [{"id": 185, "text": "[9]", "bbox": {"l": 321.198, "t": 579.83536, "r": 329.45999, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature", "bbox": {"l": 331.21487, "t": 579.83536, "r": 558.19843, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "hierarchies for accurate object detection and semantic segmentation. In", "bbox": {"l": 333.39099, "t": 587.8053600000001, "r": 543.05487, "b": 594.31889, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "IEEE", "bbox": {"l": 544.98499, "t": 587.84024, "r": 558.20148, "b": 594.32587, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Conference on Computer Vision and Pattern Recognition", "bbox": {"l": 333.39099, "t": 595.81024, "r": 491.61166, "b": 602.29587, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": ", CVPR, pages 580-587.", "bbox": {"l": 491.61301, "t": 595.77536, "r": 559.27448, "b": 602.28889, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "IEEE Computer Society, jun 2014.", "bbox": {"l": 333.39099, "t": 603.74536, "r": 428.59726000000006, "b": 610.2589, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574695229530334, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "list_item", "bbox": {"l": 317.95499, "t": 611.71536, "r": 558.20203, "b": 626.20686, "coord_origin": "TOPLEFT"}, "confidence": 0.9028682708740234, "cells": [{"id": 192, "text": "[10]", "bbox": {"l": 317.95499, "t": 611.71536, "r": 329.54767, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Ross B. Girshick. Fast R-CNN. In", "bbox": {"l": 331.31268, "t": 611.71536, "r": 425.1554, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "2015 IEEE International Conference on Computer", "bbox": {"l": 426.77802, "t": 611.75024, "r": 558.20203, "b": 618.23587, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Vision", "bbox": {"l": 333.39099, "t": 619.72124, "r": 350.59537, "b": 626.20686, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": ", ICCV, pages 1440-1448. IEEE Computer Society, dec 2015.", "bbox": {"l": 350.59598, "t": 619.68637, "r": 518.58777, "b": 626.19989, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859862327575684, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "list_item", "bbox": {"l": 317.95499, "t": 627.65637, "r": 558.20142, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9127580523490906, "cells": [{"id": 197, "text": "[11]", "bbox": {"l": 317.95499, "t": 627.65637, "r": 329.5459, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards", "bbox": {"l": 331.31064, "t": 627.65637, "r": 558.20142, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "real-time object detection with region proposal networks.", "bbox": {"l": 333.39099, "t": 635.62637, "r": 497.50909, "b": 642.13989, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "IEEE Transactions on", "bbox": {"l": 500.01401, "t": 635.66124, "r": 558.19885, "b": 642.14687, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Pattern Analysis and Machine Intelligence", "bbox": {"l": 333.39099, "t": 643.6312399999999, "r": 449.38620000000003, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": ", 39(6):1137-1149, 2017.", "bbox": {"l": 449.38699, "t": 643.59637, "r": 515.74268, "b": 650.10989, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 317.95499, "t": 651.56638, "r": 559.27808, "b": 674.01989, "coord_origin": "TOPLEFT"}, "confidence": 0.9142336249351501, "cells": [{"id": 203, "text": "[12]", "bbox": {"l": 317.95499, "t": 651.56638, "r": 329.41763, "b": 658.0799, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross B. Girshick. Mask R-CNN.", "bbox": {"l": 331.16287, "t": 651.56638, "r": 559.27808, "b": 658.0799, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "In", "bbox": {"l": 333.39099, "t": 659.53638, "r": 339.35904, "b": 666.0499, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "IEEE International Conference on Computer Vision", "bbox": {"l": 341.56299, "t": 659.57124, "r": 485.8273, "b": 666.05687, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ", ICCV, pages 2980-2988.", "bbox": {"l": 485.82901, "t": 659.53638, "r": 559.27356, "b": 666.0499, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "IEEE Computer Society, Oct 2017.", "bbox": {"l": 333.39099, "t": 667.50636, "r": 429.30161000000004, "b": 674.01989, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "list_item", "bbox": {"l": 317.95499, "t": 675.47636, "r": 558.97156, "b": 705.900894, "coord_origin": "TOPLEFT"}, "confidence": 0.8895393013954163, "cells": [{"id": 209, "text": "[13]", "bbox": {"l": 317.95499, "t": 675.47636, 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By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. 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Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. 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We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9676095843315125, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table", "bbox": {"l": 331.53516, "t": 348.70233, "r": 558.19904, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "competition. In", "bbox": {"l": 333.39099, "t": 356.67236, "r": 379.36414, "b": 363.18591, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "2013 12th International Conference on Document Analysis and", "bbox": {"l": 381.37201, "t": 356.70724, "r": 558.20099, "b": 363.19287, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Recognition", "bbox": {"l": 333.39099, "t": 364.67724999999996, "r": 365.59601, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ", pages 1449-1453, 2013.", "bbox": {"l": 365.59601, "t": 364.64236, "r": 434.29489, "b": 371.15591, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. 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In", "bbox": {"l": 333.39099, "t": 388.55236999999994, "r": 367.4339, "b": 395.06592, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "2017 14th IAPR International Conference on Document Analysis and", "bbox": {"l": 369.17401, "t": 388.58725000000004, "r": 558.20422, "b": 395.07288, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Recognition (ICDAR)", "bbox": {"l": 333.39099, "t": 396.55725, "r": 390.87601, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": ", volume 01, pages 1404-1410, 2017.", "bbox": {"l": 390.87698, "t": 396.52237, "r": 492.17831, "b": 403.03592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts rdcl2017. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) , volume 01, pages 1404-1410, 2017."}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 390.92053, "r": 294.04712, "b": 443.14014, "coord_origin": "TOPLEFT"}, "confidence": 0.9839397668838501, "cells": [{"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet\u2019s other labels as specified in table 3, and also PubLayNet\u2019s List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "list_item", "id": 17, "page_no": 7, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 321.198, "t": 404.49237, "r": 558.20013, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9257484078407288, "cells": [{"id": 148, "text": "[3]", "bbox": {"l": 321.198, "t": 404.49237, "r": 329.53583, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian", "bbox": {"l": 331.30682, "t": 404.49237, "r": 558.19958, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and", "bbox": {"l": 333.39099, "t": 412.46335, "r": 558.20013, "b": 418.9769, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "Recognition (cTDaR), April 2019. http://sac.founderit.com/.", "bbox": {"l": 333.39099, "t": 420.43335, "r": 501.80127, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[3] Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and Recognition (cTDaR), April 2019. http://sac.founderit.com/."}, {"label": "list_item", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "list_item", "bbox": {"l": 321.198, "t": 428.40335, "r": 559.37872, "b": 458.8269, "coord_origin": "TOPLEFT"}, "confidence": 0.9270963668823242, "cells": [{"id": 152, "text": "[4]", "bbox": {"l": 321.198, "t": 428.40335, "r": 329.91299, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on", "bbox": {"l": 331.7641, "t": 428.40335, "r": 558.19885, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "scientific literature parsing. In", "bbox": {"l": 333.39099, "t": 436.3733500000001, "r": 423.77225, "b": 442.8869, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Proceedings of the International Conference on", "bbox": {"l": 425.909, "t": 436.40823, "r": 558.20178, "b": 442.89386, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 444.37823, "r": 429.42697, "b": 450.86386, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": ", ICDAR, pages 605-617. LNCS 12824, Springer-", "bbox": {"l": 429.42400999999995, "t": 444.34335, "r": 559.37872, "b": 450.85689999999994, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Verlag, sep 2021.", "bbox": {"l": 332.819, "t": 452.31335, "r": 380.01764, "b": 458.8269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[4] Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on scientific literature parsing. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 605-617. LNCS 12824, SpringerVerlag, sep 2021."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "list_item", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 321.198, "t": 460.28336, "r": 559.02625, "b": 490.70792, "coord_origin": "TOPLEFT"}, "confidence": 0.9289959073066711, "cells": [{"id": 159, "text": "[5]", "bbox": {"l": 321.198, "t": 460.28336, "r": 329.22977, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin,", "bbox": {"l": 330.93576, "t": 460.28336, "r": 558.97156, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis:", "bbox": {"l": 333.39099, "t": 468.25336, "r": 559.02625, "b": 474.76691, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "not dead yet.", "bbox": {"l": 333.39099, "t": 476.22437, "r": 368.85431, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "International Journal on Document Analysis and Recognition (IJDAR)", "bbox": {"l": 370.811, "t": 476.25925, "r": 557.46326, "b": 482.74487, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": ",", "bbox": {"l": 557.46503, "t": 476.22437, "r": 558.96857, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "pages 1-11, 01 2022.", "bbox": {"l": 333.39099, "t": 484.19437, "r": 390.82715, "b": 490.70792, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[5] Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin, Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis: not dead yet. International Journal on Document Analysis and Recognition (IJDAR) , pages 1-11, 01 2022."}, {"label": "list_item", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 321.198, "t": 492.16437, "r": 558.20361, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.927467405796051, "cells": [{"id": 166, "text": "[6]", "bbox": {"l": 321.198, "t": 492.16437, "r": 329.42145, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset", "bbox": {"l": 331.16812, "t": 492.16437, "r": 558.20361, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "ever for document layout analysis. In", "bbox": {"l": 333.39099, "t": 500.13437, "r": 438.6101100000001, "b": 506.64792, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "Proceedings of the International Conference", "bbox": {"l": 440.349, "t": 500.16925, "r": 558.19958, "b": 506.65488, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "on Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 508.13925, "r": 441.40118, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": ", ICDAR, pages 1015-1022, sep 2019.", "bbox": {"l": 441.4019799999999, "t": 508.10437, "r": 544.78162, "b": 514.61792, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[6] Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset ever for document layout analysis. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 1015-1022, sep 2019."}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 321.19797, "t": 516.07437, "r": 558.9715, "b": 554.46889, "coord_origin": "TOPLEFT"}, "confidence": 0.9426887035369873, "cells": [{"id": 172, "text": "[7]", "bbox": {"l": 321.19797, "t": 516.07437, "r": 329.74161, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and", "bbox": {"l": 331.55634, "t": 516.07437, "r": 558.19897, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In", "bbox": {"l": 333.39099, "t": 524.0443700000001, "r": 558.19891, "b": 530.55792, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "Proceedings of the 28th International Conference on Computational Linguistics", "bbox": {"l": 333.39099, "t": 532.04922, "r": 557.40228, "b": 538.53487, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": ",", "bbox": {"l": 557.40399, "t": 532.01437, "r": 558.96893, "b": 538.5278900000001, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "COLING, pages 949-960. International Committee on Computational Linguistics,", "bbox": {"l": 333.39099, "t": 539.98438, "r": 558.9715, "b": 546.49789, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "dec 2020.", "bbox": {"l": 333.39099, "t": 547.95535, "r": 359.31955, "b": 554.46889, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[7] Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In Proceedings of the 28th International Conference on Computational Linguistics , COLING, pages 949-960. International Committee on Computational Linguistics, dec 2020."}, {"label": "list_item", "id": 19, "page_no": 7, "cluster": {"id": 19, "label": "list_item", "bbox": {"l": 321.198, "t": 555.92535, "r": 558.90222, "b": 578.38586, "coord_origin": "TOPLEFT"}, "confidence": 0.9139776229858398, "cells": [{"id": 179, "text": "[8]", "bbox": {"l": 321.198, "t": 555.92535, "r": 329.7088, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction", "bbox": {"l": 331.51654, "t": 555.92535, "r": 558.19891, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "from pdf sources based on rule-based system using integrated formats. In", "bbox": {"l": 333.39099, "t": 563.89536, "r": 535.54352, "b": 570.40889, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "SemWe-", "bbox": {"l": 536.96399, "t": 563.93024, "r": 558.90222, "b": 570.4158600000001, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "bEval@ESWC", "bbox": {"l": 333.39099, "t": 571.9002399999999, "r": 371.94217, "b": 578.38586, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": ", 2016.", "bbox": {"l": 371.94299, "t": 571.86536, "r": 389.72617, "b": 578.3788900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[8] Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction from pdf sources based on rule-based system using integrated formats. In SemWebEval@ESWC , 2016."}, {"label": "list_item", "id": 21, "page_no": 7, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 321.198, "t": 579.83536, "r": 559.27448, "b": 610.2589, "coord_origin": "TOPLEFT"}, "confidence": 0.9113092422485352, "cells": [{"id": 185, "text": "[9]", "bbox": {"l": 321.198, "t": 579.83536, "r": 329.45999, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature", "bbox": {"l": 331.21487, "t": 579.83536, "r": 558.19843, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "hierarchies for accurate object detection and semantic segmentation. In", "bbox": {"l": 333.39099, "t": 587.8053600000001, "r": 543.05487, "b": 594.31889, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "IEEE", "bbox": {"l": 544.98499, "t": 587.84024, "r": 558.20148, "b": 594.32587, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Conference on Computer Vision and Pattern Recognition", "bbox": {"l": 333.39099, "t": 595.81024, "r": 491.61166, "b": 602.29587, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": ", CVPR, pages 580-587.", "bbox": {"l": 491.61301, "t": 595.77536, "r": 559.27448, "b": 602.28889, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "IEEE Computer Society, jun 2014.", "bbox": {"l": 333.39099, "t": 603.74536, "r": 428.59726000000006, "b": 610.2589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[9] Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition , CVPR, pages 580-587. IEEE Computer Society, jun 2014."}, {"label": "section_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574695229530334, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example Predictions"}, {"label": "list_item", "id": 22, "page_no": 7, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 317.95499, "t": 611.71536, "r": 558.20203, "b": 626.20686, "coord_origin": "TOPLEFT"}, "confidence": 0.9028682708740234, "cells": [{"id": 192, "text": "[10]", "bbox": {"l": 317.95499, "t": 611.71536, "r": 329.54767, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Ross B. Girshick. Fast R-CNN. In", "bbox": {"l": 331.31268, "t": 611.71536, "r": 425.1554, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "2015 IEEE International Conference on Computer", "bbox": {"l": 426.77802, "t": 611.75024, "r": 558.20203, "b": 618.23587, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Vision", "bbox": {"l": 333.39099, "t": 619.72124, "r": 350.59537, "b": 626.20686, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": ", ICCV, pages 1440-1448. IEEE Computer Society, dec 2015.", "bbox": {"l": 350.59598, "t": 619.68637, "r": 518.58777, "b": 626.19989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[10] Ross B. Girshick. Fast R-CNN. In 2015 IEEE International Conference on Computer Vision , ICCV, pages 1440-1448. IEEE Computer Society, dec 2015."}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859862327575684, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "list_item", "id": 20, "page_no": 7, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 317.95499, "t": 627.65637, "r": 558.20142, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9127580523490906, "cells": [{"id": 197, "text": "[11]", "bbox": {"l": 317.95499, "t": 627.65637, "r": 329.5459, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards", "bbox": {"l": 331.31064, "t": 627.65637, "r": 558.20142, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "real-time object detection with region proposal networks.", "bbox": {"l": 333.39099, "t": 635.62637, "r": 497.50909, "b": 642.13989, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "IEEE Transactions on", "bbox": {"l": 500.01401, "t": 635.66124, "r": 558.19885, "b": 642.14687, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Pattern Analysis and Machine Intelligence", "bbox": {"l": 333.39099, "t": 643.6312399999999, "r": 449.38620000000003, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": ", 39(6):1137-1149, 2017.", "bbox": {"l": 449.38699, "t": 643.59637, "r": 515.74268, "b": 650.10989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[11] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence , 39(6):1137-1149, 2017."}, {"label": "list_item", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 317.95499, "t": 651.56638, "r": 559.27808, "b": 674.01989, "coord_origin": "TOPLEFT"}, "confidence": 0.9142336249351501, "cells": [{"id": 203, "text": "[12]", "bbox": {"l": 317.95499, "t": 651.56638, "r": 329.41763, "b": 658.0799, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross B. Girshick. Mask R-CNN.", "bbox": {"l": 331.16287, "t": 651.56638, "r": 559.27808, "b": 658.0799, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "In", "bbox": {"l": 333.39099, "t": 659.53638, "r": 339.35904, "b": 666.0499, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "IEEE International Conference on Computer Vision", "bbox": {"l": 341.56299, "t": 659.57124, "r": 485.8273, "b": 666.05687, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ", ICCV, pages 2980-2988.", "bbox": {"l": 485.82901, "t": 659.53638, "r": 559.27356, "b": 666.0499, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "IEEE Computer Society, Oct 2017.", "bbox": {"l": 333.39099, "t": 667.50636, "r": 429.30161000000004, "b": 674.01989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[12] Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross B. Girshick. Mask R-CNN. In IEEE International Conference on Computer Vision , ICCV, pages 2980-2988. IEEE Computer Society, Oct 2017."}, {"label": "list_item", "id": 23, "page_no": 7, "cluster": {"id": 23, "label": "list_item", "bbox": {"l": 317.95499, "t": 675.47636, "r": 558.97156, "b": 705.900894, "coord_origin": "TOPLEFT"}, "confidence": 0.8895393013954163, "cells": [{"id": 209, "text": "[13]", "bbox": {"l": 317.95499, "t": 675.47636, "r": 330.11407, "b": 681.9898900000001, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012,", "bbox": {"l": 331.96533, "t": 675.47636, "r": 558.96716, "b": 681.9898900000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "bbox": {"l": 333.18201, "t": 683.44637, "r": 558.96661, "b": 689.95989, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "bbox": {"l": 333.39099, "t": 691.41737, "r": 558.97156, "b": 697.930893, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "bbox": {"l": 333.39099, "t": 699.387367, "r": 558.20001, "b": 705.900894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu"}], "body": [{"label": "section_header", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319990873336792, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 CONCLUSION"}, {"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64868, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9824119210243225, "cells": [{"id": 2, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.32153, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 295.64868, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "cLayNet data-sets. By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"label": "table", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "table", "bbox": {"l": 72.65901184082031, "t": 172.4807891845703, "r": 274.8346862792969, "b": 339.85400390625, "coord_origin": "TOPLEFT"}, "confidence": 0.9892617464065552, "cells": [{"id": 8, "text": "Testing on", "bbox": {"l": 217.74099999999999, "t": 175.01855, "r": 256.26065, "b": 183.39319, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Training on", "bbox": {"l": 89.954002, "t": 185.97655999999995, "r": 133.24379, "b": 194.35119999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "labels", "bbox": {"l": 154.629, "t": 185.97655999999995, "r": 175.47588, "b": 194.35119999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "PLN", "bbox": {"l": 204.69, "t": 185.97655999999995, "r": 220.54260000000002, "b": 194.35119999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": 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We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9676095843315125, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table", "bbox": {"l": 331.53516, "t": 348.70233, "r": 558.19904, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "competition. In", "bbox": {"l": 333.39099, "t": 356.67236, "r": 379.36414, "b": 363.18591, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "2013 12th International Conference on Document Analysis and", "bbox": {"l": 381.37201, "t": 356.70724, "r": 558.20099, "b": 363.19287, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Recognition", "bbox": {"l": 333.39099, "t": 364.67724999999996, "r": 365.59601, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ", pages 1449-1453, 2013.", "bbox": {"l": 365.59601, "t": 364.64236, "r": 434.29489, "b": 371.15591, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. In 2013 12th International Conference on Document Analysis and Recognition , pages 1449-1453, 2013."}, {"label": "list_item", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 321.198, "t": 372.61237, "r": 559.37982, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9480941295623779, "cells": [{"id": 141, "text": "[2]", "bbox": {"l": 321.198, "t": 372.61237, "r": 329.85956, "b": 379.12592, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Ic-", "bbox": {"l": 331.69931, "t": 372.61237, "r": 559.37976, "b": 379.12592, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "dar2017 competition on recognition of documents with complex layouts -", "bbox": {"l": 333.39099, "t": 380.58237, "r": 559.37982, "b": 387.09592, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "rdcl2017. In", "bbox": {"l": 333.39099, "t": 388.55236999999994, "r": 367.4339, "b": 395.06592, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "2017 14th IAPR International Conference on Document Analysis and", "bbox": {"l": 369.17401, "t": 388.58725000000004, "r": 558.20422, "b": 395.07288, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Recognition (ICDAR)", "bbox": {"l": 333.39099, "t": 396.55725, "r": 390.87601, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": ", volume 01, pages 1404-1410, 2017.", "bbox": {"l": 390.87698, "t": 396.52237, "r": 492.17831, "b": 403.03592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts rdcl2017. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) , volume 01, pages 1404-1410, 2017."}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 390.92053, "r": 294.04712, "b": 443.14014, "coord_origin": "TOPLEFT"}, "confidence": 0.9839397668838501, "cells": [{"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet\u2019s other labels as specified in table 3, and also PubLayNet\u2019s List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "list_item", "id": 17, "page_no": 7, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 321.198, "t": 404.49237, "r": 558.20013, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9257484078407288, "cells": [{"id": 148, "text": "[3]", "bbox": {"l": 321.198, "t": 404.49237, "r": 329.53583, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian", "bbox": {"l": 331.30682, "t": 404.49237, "r": 558.19958, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and", "bbox": {"l": 333.39099, "t": 412.46335, "r": 558.20013, "b": 418.9769, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "Recognition (cTDaR), April 2019. http://sac.founderit.com/.", "bbox": {"l": 333.39099, "t": 420.43335, "r": 501.80127, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[3] Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and Recognition (cTDaR), April 2019. http://sac.founderit.com/."}, {"label": "list_item", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "list_item", "bbox": {"l": 321.198, "t": 428.40335, "r": 559.37872, "b": 458.8269, "coord_origin": "TOPLEFT"}, "confidence": 0.9270963668823242, "cells": [{"id": 152, "text": "[4]", "bbox": {"l": 321.198, "t": 428.40335, "r": 329.91299, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on", "bbox": {"l": 331.7641, "t": 428.40335, "r": 558.19885, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "scientific literature parsing. In", "bbox": {"l": 333.39099, "t": 436.3733500000001, "r": 423.77225, "b": 442.8869, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Proceedings of the International Conference on", "bbox": {"l": 425.909, "t": 436.40823, "r": 558.20178, "b": 442.89386, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 444.37823, "r": 429.42697, "b": 450.86386, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": ", ICDAR, pages 605-617. LNCS 12824, Springer-", "bbox": {"l": 429.42400999999995, "t": 444.34335, "r": 559.37872, "b": 450.85689999999994, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Verlag, sep 2021.", "bbox": {"l": 332.819, "t": 452.31335, "r": 380.01764, "b": 458.8269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[4] Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on scientific literature parsing. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 605-617. LNCS 12824, SpringerVerlag, sep 2021."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "list_item", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 321.198, "t": 460.28336, "r": 559.02625, "b": 490.70792, "coord_origin": "TOPLEFT"}, "confidence": 0.9289959073066711, "cells": [{"id": 159, "text": "[5]", "bbox": {"l": 321.198, "t": 460.28336, "r": 329.22977, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin,", "bbox": {"l": 330.93576, "t": 460.28336, "r": 558.97156, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis:", "bbox": {"l": 333.39099, "t": 468.25336, "r": 559.02625, "b": 474.76691, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "not dead yet.", "bbox": {"l": 333.39099, "t": 476.22437, "r": 368.85431, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "International Journal on Document Analysis and Recognition (IJDAR)", "bbox": {"l": 370.811, "t": 476.25925, "r": 557.46326, "b": 482.74487, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": ",", "bbox": {"l": 557.46503, "t": 476.22437, "r": 558.96857, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "pages 1-11, 01 2022.", "bbox": {"l": 333.39099, "t": 484.19437, "r": 390.82715, "b": 490.70792, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[5] Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin, Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis: not dead yet. International Journal on Document Analysis and Recognition (IJDAR) , pages 1-11, 01 2022."}, {"label": "list_item", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 321.198, "t": 492.16437, "r": 558.20361, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.927467405796051, "cells": [{"id": 166, "text": "[6]", "bbox": {"l": 321.198, "t": 492.16437, "r": 329.42145, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset", "bbox": {"l": 331.16812, "t": 492.16437, "r": 558.20361, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "ever for document layout analysis. In", "bbox": {"l": 333.39099, "t": 500.13437, "r": 438.6101100000001, "b": 506.64792, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "Proceedings of the International Conference", "bbox": {"l": 440.349, "t": 500.16925, "r": 558.19958, "b": 506.65488, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "on Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 508.13925, "r": 441.40118, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": ", ICDAR, pages 1015-1022, sep 2019.", "bbox": {"l": 441.4019799999999, "t": 508.10437, "r": 544.78162, "b": 514.61792, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[6] Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset ever for document layout analysis. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 1015-1022, sep 2019."}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 321.19797, "t": 516.07437, "r": 558.9715, "b": 554.46889, "coord_origin": "TOPLEFT"}, "confidence": 0.9426887035369873, "cells": [{"id": 172, "text": "[7]", "bbox": {"l": 321.19797, "t": 516.07437, "r": 329.74161, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and", "bbox": {"l": 331.55634, "t": 516.07437, "r": 558.19897, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In", "bbox": {"l": 333.39099, "t": 524.0443700000001, "r": 558.19891, "b": 530.55792, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "Proceedings of the 28th International Conference on Computational Linguistics", "bbox": {"l": 333.39099, "t": 532.04922, "r": 557.40228, "b": 538.53487, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": ",", "bbox": {"l": 557.40399, "t": 532.01437, "r": 558.96893, "b": 538.5278900000001, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "COLING, pages 949-960. International Committee on Computational Linguistics,", "bbox": {"l": 333.39099, "t": 539.98438, "r": 558.9715, "b": 546.49789, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "dec 2020.", "bbox": {"l": 333.39099, "t": 547.95535, "r": 359.31955, "b": 554.46889, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[7] Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In Proceedings of the 28th International Conference on Computational Linguistics , COLING, pages 949-960. International Committee on Computational Linguistics, dec 2020."}, {"label": "list_item", "id": 19, "page_no": 7, "cluster": {"id": 19, "label": "list_item", "bbox": {"l": 321.198, "t": 555.92535, "r": 558.90222, "b": 578.38586, "coord_origin": "TOPLEFT"}, "confidence": 0.9139776229858398, "cells": [{"id": 179, "text": "[8]", "bbox": {"l": 321.198, "t": 555.92535, "r": 329.7088, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction", "bbox": {"l": 331.51654, "t": 555.92535, "r": 558.19891, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "from pdf sources based on rule-based system using integrated formats. In", "bbox": {"l": 333.39099, "t": 563.89536, "r": 535.54352, "b": 570.40889, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "SemWe-", "bbox": {"l": 536.96399, "t": 563.93024, "r": 558.90222, "b": 570.4158600000001, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "bEval@ESWC", "bbox": {"l": 333.39099, "t": 571.9002399999999, "r": 371.94217, "b": 578.38586, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": ", 2016.", "bbox": {"l": 371.94299, "t": 571.86536, "r": 389.72617, "b": 578.3788900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[8] Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction from pdf sources based on rule-based system using integrated formats. In SemWebEval@ESWC , 2016."}, {"label": "list_item", "id": 21, "page_no": 7, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 321.198, "t": 579.83536, "r": 559.27448, "b": 610.2589, "coord_origin": "TOPLEFT"}, "confidence": 0.9113092422485352, "cells": [{"id": 185, "text": "[9]", "bbox": {"l": 321.198, "t": 579.83536, "r": 329.45999, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature", "bbox": {"l": 331.21487, "t": 579.83536, "r": 558.19843, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "hierarchies for accurate object detection and semantic segmentation. In", "bbox": {"l": 333.39099, "t": 587.8053600000001, "r": 543.05487, "b": 594.31889, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "IEEE", "bbox": {"l": 544.98499, "t": 587.84024, "r": 558.20148, "b": 594.32587, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Conference on Computer Vision and Pattern Recognition", "bbox": {"l": 333.39099, "t": 595.81024, "r": 491.61166, "b": 602.29587, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": ", CVPR, pages 580-587.", "bbox": {"l": 491.61301, "t": 595.77536, "r": 559.27448, "b": 602.28889, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "IEEE Computer Society, jun 2014.", "bbox": {"l": 333.39099, "t": 603.74536, "r": 428.59726000000006, "b": 610.2589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[9] Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition , CVPR, pages 580-587. IEEE Computer Society, jun 2014."}, {"label": "section_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574695229530334, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example Predictions"}, {"label": "list_item", "id": 22, "page_no": 7, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 317.95499, "t": 611.71536, "r": 558.20203, "b": 626.20686, "coord_origin": "TOPLEFT"}, "confidence": 0.9028682708740234, "cells": [{"id": 192, "text": "[10]", "bbox": {"l": 317.95499, "t": 611.71536, "r": 329.54767, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Ross B. Girshick. 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IEEE Computer Society, dec 2015."}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859862327575684, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "list_item", "id": 20, "page_no": 7, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 317.95499, "t": 627.65637, "r": 558.20142, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9127580523490906, "cells": [{"id": 197, "text": "[11]", "bbox": {"l": 317.95499, "t": 627.65637, "r": 329.5459, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 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IEEE Computer Society, Oct 2017."}, {"label": "list_item", "id": 23, "page_no": 7, "cluster": {"id": 23, "label": "list_item", "bbox": {"l": 317.95499, "t": 675.47636, "r": 558.97156, "b": 705.900894, "coord_origin": "TOPLEFT"}, "confidence": 0.8895393013954163, "cells": [{"id": 209, "text": "[13]", "bbox": {"l": 317.95499, "t": 675.47636, "r": 330.11407, "b": 681.9898900000001, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012,", "bbox": {"l": 331.96533, "t": 675.47636, "r": 558.96716, "b": 681.9898900000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "bbox": {"l": 333.18201, "t": 683.44637, "r": 558.96661, "b": 689.95989, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "bbox": {"l": 333.39099, "t": 691.41737, "r": 558.97156, "b": 697.930893, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "bbox": {"l": 333.39099, "t": 699.387367, "r": 558.20001, "b": 705.900894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu"}], "headers": [{"label": "page_header", "id": 24, "page_no": 7, "cluster": {"id": 24, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.79841548204422, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "4bed2a8aa51ac37058e79605821bbc426d032b0b6ca8bdf3409ed8508ccd8c67", "bbox": {"l": 231.8804, "t": 301.50543, "r": 235.14504999999997, "b": 414.69144, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "2f2a06d08f5ad565d0f5e815f4ddf666365b2cff435cdaeb8850217e8a8efabf", "bbox": {"l": 395.06876, "t": 117.37183000000005, "r": 398.33353, "b": 230.55786, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "7f2fd7293e04bf4f1756ae51f5779764933da1d1d2002e3915356050570fc75b", "bbox": {"l": 55.775887, "t": 301.50543, "r": 59.04052000000001, "b": 414.69144, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "1b81cf65f47456ad4faa725d1eb09879bd633af16cfe2bf8cea661b87907bfac", "bbox": {"l": 232.01364, "t": 117.37183000000005, "r": 235.27841000000004, "b": 230.55786, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "b60da9d26f488cb133e47d101d35fda1bdca2671ade60764d1cd569590270327", "bbox": {"l": 395.20047, "t": 301.50543, "r": 398.46512, "b": 414.69144, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "2b7b8355a42ebef0cf91583aad9f30f7c9fa63c5b05911730ba15275c024965b$^{A}$", "bbox": {"l": 55.775818, "t": 117.37183000000005, "r": 65.409912, "b": 230.55786, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "B", "bbox": {"l": 234.56980999999996, "t": 88.50183000000015, "r": 240.06987, "b": 97.01098999999988, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 397.81934, "t": 88.89355, "r": 403.3194, "b": 97.40270999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "D", "bbox": {"l": 59.909843, "t": 266.75885000000005, "r": 65.409912, "b": 275.26793999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "E", "bbox": {"l": 234.77386, "t": 266.36707, "r": 239.85495000000003, "b": 274.87616, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "F", "bbox": {"l": 398.26144, "t": 266.75885000000005, "r": 402.91592, "b": 275.26793999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Text", "bbox": {"l": 62.323874999999994, "t": 442.28543, "r": 70.895882, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Caption", "bbox": {"l": 80.16581, "t": 442.28543, "r": 95.565453, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "List-Item", "bbox": {"l": 104.94447, "t": 442.28543, "r": 122.38113000000001, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Formula", "bbox": {"l": 131.78354, "t": 442.28543, "r": 148.34625, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Table", "bbox": {"l": 157.66106, "t": 442.28543, "r": 168.53032, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Section-Header", "bbox": {"l": 201.24315, "t": 442.28543, "r": 232.00499, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Picture", "bbox": {"l": 177.8381, "t": 442.28543, "r": 191.88956, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Page-Header", "bbox": {"l": 240.95844000000002, "t": 442.28543, "r": 266.61908, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Page-Footer", "bbox": {"l": 276.03928, "t": 442.28543, "r": 300.33261, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Title", "bbox": {"l": 309.74615, "t": 442.28543, "r": 318.50473, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on", "bbox": {"l": 53.79800000000001, "t": 464.48199, "r": 558.203, "b": 472.95523, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demon-", "bbox": {"l": 53.79800000000001, "t": 475.44101, "r": 559.80786, "b": 483.91425, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "strates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion", "bbox": {"l": 53.79800000000001, "t": 486.39999, "r": 558.20294, "b": 494.87323, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "and missing boxes.", "bbox": {"l": 53.79800000000001, "t": 497.358, "r": 130.37105, "b": 505.83124, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ul-", "bbox": {"l": 69.234001, "t": 527.06635, "r": 295.22406, "b": 533.5799, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow", "bbox": {"l": 69.234001, "t": 535.03638, "r": 294.30612, "b": 541.5499, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "export, opencv dnn support, October 2021.", "bbox": {"l": 69.234001, "t": 543.00638, "r": 190.45259, "b": 549.5199, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "[14]", "bbox": {"l": 53.79800000000001, "t": 550.97638, "r": 65.286942, "b": 557.4899, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander", "bbox": {"l": 67.036171, "t": 550.97638, "r": 294.17709, "b": 557.4899, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Kirillov, and Sergey Zagoruyko. 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(B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. 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Association for Computing Machinery."}], "headers": [{"label": "page_header", "id": 13, "page_no": 8, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8021655082702637, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8429455161094666, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}]
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diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.doctags.txt b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.doctags.txt
index 7fa7e640..d2813af7 100644
--- a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.doctags.txt
+++ b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.doctags.txt
@@ -4,14 +4,14 @@
 <paragraph><location><page_1><loc_22><loc_68><loc_79><loc_77></location>We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.</paragraph>
 <caption><location><page_1><loc_22><loc_59><loc_79><loc_66></location>Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.</caption>
 <table>
-<location><page_1><loc_24><loc_41><loc_77><loc_57></location>
+<location><page_1><loc_23><loc_41><loc_78><loc_57></location>
 <caption>Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.</caption>
 <row_0><col_0><col_header>#</col_0><col_1><col_header>#</col_1><col_2><col_header>Language</col_2><col_3><col_header>TEDs</col_3><col_4><col_header>TEDs</col_4><col_5><col_header>TEDs</col_5><col_6><col_header>mAP</col_6><col_7><col_header>Inference</col_7></row_0>
 <row_1><col_0><col_header>enc-layers</col_0><col_1><col_header>dec-layers</col_1><col_2><col_header>Language</col_2><col_3><col_header>simple</col_3><col_4><col_header>complex</col_4><col_5><col_header>all</col_5><col_6><col_header>(0.75)</col_6><col_7><col_header>time (secs)</col_7></row_1>
 <row_2><col_0><body>6</col_0><col_1><body>6</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.965 0.969</col_3><col_4><body>0.934 0.927</col_4><col_5><body>0.955 0.955</col_5><col_6><body>0.88 0.857</col_6><col_7><body>2.73 5.39</col_7></row_2>
-<row_3><col_0><body>4</col_0><col_1><body>4</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.938 0.952</col_3><col_4><body>0.904</col_4><col_5><body>0.927</col_5><col_6><body>0.853</col_6><col_7><body>1.97</col_7></row_3>
-<row_4><col_0><body>2</col_0><col_1><body>4</col_1><col_2><body>OTSL</col_2><col_3><body>0.923 0.945</col_3><col_4><body>0.909 0.897</col_4><col_5><body>0.938</col_5><col_6><body>0.843</col_6><col_7><body>3.77</col_7></row_4>
-<row_5><col_0><body></col_0><col_1><body></col_1><col_2><body>HTML</col_2><col_3><body></col_3><col_4><body>0.901</col_4><col_5><body>0.915 0.931</col_5><col_6><body>0.859 0.834</col_6><col_7><body>1.91 3.81</col_7></row_5>
+<row_3><col_0><body>4</col_0><col_1><body>4</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.938</col_3><col_4><body>0.904</col_4><col_5><body>0.927</col_5><col_6><body>0.853</col_6><col_7><body>1.97</col_7></row_3>
+<row_4><col_0><body></col_0><col_1><body></col_1><col_2><body>OTSL</col_2><col_3><body>0.952 0.923</col_3><col_4><body>0.909</col_4><col_5><body>0.938</col_5><col_6><body>0.843</col_6><col_7><body>3.77</col_7></row_4>
+<row_5><col_0><body>2</col_0><col_1><body>4</col_1><col_2><body>HTML</col_2><col_3><body>0.945</col_3><col_4><body>0.897 0.901</col_4><col_5><body>0.915 0.931</col_5><col_6><body>0.859 0.834</col_6><col_7><body>1.91 3.81</col_7></row_5>
 <row_6><col_0><body>4</col_0><col_1><body>2</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.952 0.944</col_3><col_4><body>0.92 0.903</col_4><col_5><body>0.942 0.931</col_5><col_6><body>0.857 0.824</col_6><col_7><body>1.22 2</col_7></row_6>
 </table>
 <subtitle-level-1><location><page_1><loc_22><loc_35><loc_43><loc_36></location>5.2 Quantitative Results</subtitle-level-1>
diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.json b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.json
index 4f3c49a0..d5036268 100644
--- a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.json
+++ b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.json
@@ -1 +1 @@
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Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 612.7918090820312, 318.4514465332031, 625.2948608398438], "page": 1, "span": [0, 32], "__ref_s3_data": null}], "text": "5.1 Hyper Parameter Optimization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 536.5759887695312, 480.5956726074219, 608.8849487304688], "page": 1, "span": [0, 423], "__ref_s3_data": null}], "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. 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Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 612.7918090820312, 318.4514465332031, 625.2948608398438], "page": 1, "span": [0, 32], "__ref_s3_data": null}], "text": "5.1 Hyper Parameter Optimization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 536.5759887695312, 480.5956726074219, 608.8849487304688], "page": 1, "span": [0, 423], "__ref_s3_data": null}], "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 464.017822265625, 480.5989074707031, 519.2052612304688], "page": 1, "span": [0, 398], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [134.76499938964844, 273.8258056640625, 264.4082946777344, 286.3288879394531], "page": 1, "span": [0, 24], "__ref_s3_data": null}], "text": "5.2 Quantitative Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 173.6999969482422, 480.72003173828125, 269.9199523925781], "page": 1, "span": [0, 555], "__ref_s3_data": null}], "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 125.87999725341797, 480.59857177734375, 174.2779541015625], "page": 1, "span": [0, 289], "__ref_s3_data": null}], "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. 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\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.md b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.md
index 27e932bc..45466f7d 100644
--- a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.md
+++ b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.md
@@ -10,9 +10,9 @@ Table 1. HPO performed in OTSL and HTML representation on the same transformer-b
 |------------|------------|------------|-------------|-------------|-------------|-------------|-------------|
 | enc-layers | dec-layers | Language   | simple      | complex     | all         | (0.75)      | time (secs) |
 | 6          | 6          | OTSL HTML  | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857  | 2.73 5.39   |
-| 4          | 4          | OTSL HTML  | 0.938 0.952 | 0.904       | 0.927       | 0.853       | 1.97        |
-| 2          | 4          | OTSL       | 0.923 0.945 | 0.909 0.897 | 0.938       | 0.843       | 3.77        |
-|            |            | HTML       |             | 0.901       | 0.915 0.931 | 0.859 0.834 | 1.91 3.81   |
+| 4          | 4          | OTSL HTML  | 0.938       | 0.904       | 0.927       | 0.853       | 1.97        |
+|            |            | OTSL       | 0.952 0.923 | 0.909       | 0.938       | 0.843       | 3.77        |
+| 2          | 4          | HTML       | 0.945       | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81   |
 | 4          | 2          | OTSL HTML  | 0.952 0.944 | 0.92 0.903  | 0.942 0.931 | 0.857 0.824 | 1.22 2      |
 
 ## 5.2 Quantitative Results
diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.pages.json b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.pages.json
index 363a49b3..c25f672e 100644
--- a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.pages.json
+++ b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Language", "bbox": {"l": 239.79799999999997, "t": 344.93649, "r": 278.3338, "b": 356.22519000000005, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TEDs", "bbox": {"l": 324.67001, "t": 339.45749, "r": 348.26419, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "mAP", "bbox": {"l": 396.271, "t": 339.45749, "r": 417.12595, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "(0.75)", "bbox": {"l": 394.927, "t": 350.41647, "r": 418.46921, "b": 361.70517, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Inference", "bbox": {"l": 430.771, "t": 339.45749, "r": 467.14142000000004, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 31, 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{"id": 83, "text": "0.857", "bbox": {"l": 394.61801, "t": 444.5996999999999, "r": 418.77798, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1.22", "bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 457.61447, "r": 272.94495, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "0.944", "bbox": {"l": 289.017, "t": 457.61447, "r": 310.00732, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0.903", "bbox": {"l": 326.71701, "t": 457.61447, "r": 347.70734, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "0.931", "bbox": {"l": 363.67599, "t": 457.61447, "r": 384.66632, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0.824", "bbox": {"l": 396.20599, "t": 457.61447, "r": 417.19632, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "2", "bbox": {"l": 446.65302, "t": 457.61447, "r": 451.26175, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373531937599182, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.957740306854248, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591906070709229, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 144.592, "t": 339.45749, "r": 470.76955999999996, "b": 468.90317, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Language", "bbox": {"l": 239.79799999999997, "t": 344.93649, "r": 278.3338, "b": 356.22519000000005, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TEDs", "bbox": {"l": 324.67001, "t": 339.45749, "r": 348.26419, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "mAP", "bbox": {"l": 396.271, "t": 339.45749, "r": 417.12595, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "(0.75)", "bbox": {"l": 394.927, "t": 350.41647, "r": 418.46921, "b": 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Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.957740306854248, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591906070709229, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 144.592, "t": 339.45749, "r": 470.76955999999996, "b": 468.90317, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 363.69717, "coord_origin": 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It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.957740306854248, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591906070709229, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 144.592, "t": 339.45749, "r": 470.76955999999996, "b": 468.90317, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 363.69717, "coord_origin": 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6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 450.14248999999995, "r": 214.11774, "b": 461.43118, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.17598999999998, "t": 444.66248, "r": 272.94495, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 444.66248, "r": 310.00732, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, 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"start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.857 0.824", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589295387268066, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849976301193237, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373531937599182, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9"}]}}]
\ No newline at end of file
+[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 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{"id": 83, "text": "0.857", "bbox": {"l": 394.61801, "t": 444.5996999999999, "r": 418.77798, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1.22", "bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 457.61447, "r": 272.94495, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "0.944", "bbox": {"l": 289.017, "t": 457.61447, "r": 310.00732, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0.903", "bbox": {"l": 326.71701, "t": 457.61447, "r": 347.70734, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "0.931", "bbox": {"l": 363.67599, "t": 457.61447, "r": 384.66632, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0.824", "bbox": {"l": 396.20599, "t": 457.61447, "r": 417.19632, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "2", "bbox": {"l": 446.65302, "t": 457.61447, "r": 451.26175, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373531937599182, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.957740306854248, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591906070709229, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Language", "bbox": {"l": 239.79799999999997, "t": 344.93649, "r": 278.3338, "b": 356.22519000000005, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TEDs", "bbox": {"l": 324.67001, "t": 339.45749, "r": 348.26419, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "mAP", "bbox": {"l": 396.271, "t": 339.45749, "r": 417.12595, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "(0.75)", "bbox": {"l": 394.927, "t": 350.41647, 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Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.957740306854248, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591906070709229, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 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480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.957740306854248, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591906070709229, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 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"col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 444.66248, "r": 310.00732, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.952 0.944", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.71701, "t": 444.66248, "r": 347.70734, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.92 0.903", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 362.08801, "t": 444.5996999999999, "r": 386.24799, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.942 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.61801, "t": 444.5996999999999, "r": 418.77798, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.857 0.824", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589295387268066, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849976301193237, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373531937599182, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9"}]}}]
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1.doctags.txt b/tests/data/groundtruth/docling_v1/2305.03393v1.doctags.txt
index fbd72a26..776c5945 100644
--- a/tests/data/groundtruth/docling_v1/2305.03393v1.doctags.txt
+++ b/tests/data/groundtruth/docling_v1/2305.03393v1.doctags.txt
@@ -79,31 +79,31 @@
 <paragraph><location><page_9><loc_22><loc_68><loc_79><loc_77></location>We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.</paragraph>
 <caption><location><page_9><loc_22><loc_59><loc_79><loc_65></location>Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.</caption>
 <table>
-<location><page_9><loc_24><loc_41><loc_77><loc_57></location>
+<location><page_9><loc_23><loc_41><loc_78><loc_57></location>
 <caption>Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.</caption>
 <row_0><col_0><col_header>#</col_0><col_1><col_header>#</col_1><col_2><col_header>Language</col_2><col_3><col_header>TEDs</col_3><col_4><col_header>TEDs</col_4><col_5><col_header>TEDs</col_5><col_6><col_header>mAP</col_6><col_7><col_header>Inference</col_7></row_0>
-<row_1><col_0><col_header>enc-layers</col_0><col_1><col_header>dec-layers</col_1><col_2><body></col_2><col_3><col_header>simple</col_3><col_4><col_header>complex</col_4><col_5><col_header>all</col_5><col_6><col_header>(0.75)</col_6><col_7><col_header>time (secs)</col_7></row_1>
+<row_1><col_0><col_header>enc-layers</col_0><col_1><col_header>dec-layers</col_1><col_2><col_header>Language</col_2><col_3><col_header>simple</col_3><col_4><col_header>complex</col_4><col_5><col_header>all</col_5><col_6><col_header>(0.75)</col_6><col_7><col_header>time (secs)</col_7></row_1>
 <row_2><col_0><body>6</col_0><col_1><body>6</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.965 0.969</col_3><col_4><body>0.934 0.927</col_4><col_5><body>0.955 0.955</col_5><col_6><body>0.88 0.857</col_6><col_7><body>2.73 5.39</col_7></row_2>
-<row_3><col_0><body>4</col_0><col_1><body>4</col_1><col_2><body>OTSL</col_2><col_3><body>0.938</col_3><col_4><body>0.904</col_4><col_5><body>0.927</col_5><col_6><body>0.853</col_6><col_7><body>1.97</col_7></row_3>
-<row_4><col_0><body></col_0><col_1><body></col_1><col_2><body>HTML</col_2><col_3><body>0.952</col_3><col_4><body>0.909</col_4><col_5><body>0.938</col_5><col_6><body>0.843</col_6><col_7><body>3.77</col_7></row_4>
-<row_5><col_0><body>2</col_0><col_1><body>4</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.923 0.945</col_3><col_4><body>0.897 0.901</col_4><col_5><body>0.915 0.931</col_5><col_6><body>0.859 0.834</col_6><col_7><body>1.91</col_7></row_5>
-<row_6><col_0><body>4</col_0><col_1><body>2</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.952 0.944</col_3><col_4><body>0.92 0.903</col_4><col_5><body>0.942 0.931</col_5><col_6><body>0.857 0.824</col_6><col_7><body>3.81 1.22 2</col_7></row_6>
+<row_3><col_0><body>4</col_0><col_1><body>4</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.938 0.952</col_3><col_4><body>0.904</col_4><col_5><body>0.927</col_5><col_6><body>0.853</col_6><col_7><body>1.97</col_7></row_3>
+<row_4><col_0><body>2</col_0><col_1><body>4</col_1><col_2><body>OTSL</col_2><col_3><body>0.923 0.945</col_3><col_4><body>0.909 0.897</col_4><col_5><body>0.938</col_5><col_6><body>0.843</col_6><col_7><body>3.77</col_7></row_4>
+<row_5><col_0><body></col_0><col_1><body></col_1><col_2><body>HTML</col_2><col_3><body></col_3><col_4><body>0.901</col_4><col_5><body>0.915 0.931</col_5><col_6><body>0.859 0.834</col_6><col_7><body>1.91 3.81</col_7></row_5>
+<row_6><col_0><body>4</col_0><col_1><body>2</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.952 0.944</col_3><col_4><body>0.92 0.903</col_4><col_5><body>0.942 0.931</col_5><col_6><body>0.857 0.824</col_6><col_7><body>1.22 2</col_7></row_6>
 </table>
 <subtitle-level-1><location><page_9><loc_22><loc_35><loc_43><loc_36></location>5.2 Quantitative Results</subtitle-level-1>
 <paragraph><location><page_9><loc_22><loc_22><loc_79><loc_34></location>We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.</paragraph>
 <paragraph><location><page_9><loc_22><loc_16><loc_79><loc_22></location>Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.</paragraph>
 <caption><location><page_10><loc_22><loc_82><loc_79><loc_85></location>Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).</caption>
 <table>
-<location><page_10><loc_24><loc_67><loc_76><loc_80></location>
+<location><page_10><loc_23><loc_67><loc_77><loc_80></location>
 <caption>Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).</caption>
-<row_0><col_0><body></col_0><col_1><col_header>Language</col_1><col_2><col_header>TEDs</col_2><col_3><col_header>TEDs</col_3><col_4><col_header>TEDs</col_4><col_5><col_header>mAP(0.75)</col_5><col_6><col_header>Inference</col_6></row_0>
-<row_1><col_0><col_header>Data set</col_0><col_1><body></col_1><col_2><col_header>simple</col_2><col_3><col_header>complex</col_3><col_4><col_header>all</col_4><col_5><body></col_5><col_6><col_header>time (secs)</col_6></row_1>
-<row_2><col_0><row_header>PubTabNet</col_0><col_1><body>OTSL</col_1><col_2><body>0.965</col_2><col_3><body>0.934</col_3><col_4><body>0.955</col_4><col_5><body>0.88</col_5><col_6><body>2.73</col_6></row_2>
-<row_3><col_0><row_header>PubTabNet</col_0><col_1><body>HTML</col_1><col_2><body>0.969</col_2><col_3><body>0.927</col_3><col_4><body>0.955</col_4><col_5><body>0.857</col_5><col_6><body>5.39</col_6></row_3>
-<row_4><col_0><row_header>FinTabNet</col_0><col_1><body>OTSL</col_1><col_2><body>0.955</col_2><col_3><body>0.961</col_3><col_4><body>0.959</col_4><col_5><body>0.862</col_5><col_6><body>1.85</col_6></row_4>
-<row_5><col_0><row_header>FinTabNet</col_0><col_1><body>HTML</col_1><col_2><body>0.917</col_2><col_3><body>0.922</col_3><col_4><body>0.92</col_4><col_5><body>0.722</col_5><col_6><body>3.26</col_6></row_5>
-<row_6><col_0><row_header>PubTables-1M</col_0><col_1><body>OTSL</col_1><col_2><body>0.987</col_2><col_3><body>0.964</col_3><col_4><body>0.977</col_4><col_5><body>0.896</col_5><col_6><body>1.79</col_6></row_6>
-<row_7><col_0><row_header>PubTables-1M</col_0><col_1><body>HTML</col_1><col_2><body>0.983</col_2><col_3><body>0.944</col_3><col_4><body>0.966</col_4><col_5><body>0.889</col_5><col_6><body>3.26</col_6></row_7>
+<row_0><col_0><body></col_0><col_1><col_header>Language</col_1><col_2><col_header>TEDs</col_2><col_3><col_header>TEDs</col_3><col_4><col_header>TEDs</col_4><col_5><col_header>mAP(0.75)</col_5><col_6><col_header>Inference time (secs)</col_6></row_0>
+<row_1><col_0><body></col_0><col_1><col_header>Language</col_1><col_2><col_header>simple</col_2><col_3><col_header>complex</col_3><col_4><col_header>all</col_4><col_5><col_header>mAP(0.75)</col_5><col_6><col_header>Inference time (secs)</col_6></row_1>
+<row_2><col_0><row_header>PubTabNet</col_0><col_1><row_header>OTSL</col_1><col_2><body>0.965</col_2><col_3><body>0.934</col_3><col_4><body>0.955</col_4><col_5><body>0.88</col_5><col_6><body>2.73</col_6></row_2>
+<row_3><col_0><row_header>PubTabNet</col_0><col_1><row_header>HTML</col_1><col_2><body>0.969</col_2><col_3><body>0.927</col_3><col_4><body>0.955</col_4><col_5><body>0.857</col_5><col_6><body>5.39</col_6></row_3>
+<row_4><col_0><row_header>FinTabNet</col_0><col_1><row_header>OTSL</col_1><col_2><body>0.955</col_2><col_3><body>0.961</col_3><col_4><body>0.959</col_4><col_5><body>0.862</col_5><col_6><body>1.85</col_6></row_4>
+<row_5><col_0><row_header>FinTabNet</col_0><col_1><row_header>HTML</col_1><col_2><body>0.917</col_2><col_3><body>0.922</col_3><col_4><body>0.92</col_4><col_5><body>0.722</col_5><col_6><body>3.26</col_6></row_5>
+<row_6><col_0><row_header>PubTables-1M</col_0><col_1><row_header>OTSL</col_1><col_2><body>0.987</col_2><col_3><body>0.964</col_3><col_4><body>0.977</col_4><col_5><body>0.896</col_5><col_6><body>1.79</col_6></row_6>
+<row_7><col_0><row_header>PubTables-1M</col_0><col_1><row_header>HTML</col_1><col_2><body>0.983</col_2><col_3><body>0.944</col_3><col_4><body>0.966</col_4><col_5><body>0.889</col_5><col_6><body>3.26</col_6></row_7>
 </table>
 <subtitle-level-1><location><page_10><loc_22><loc_62><loc_42><loc_64></location>5.3 Qualitative Results</subtitle-level-1>
 <paragraph><location><page_10><loc_22><loc_54><loc_79><loc_61></location>To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes.</paragraph>
diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1.json b/tests/data/groundtruth/docling_v1/2305.03393v1.json
index f4a4c93c..395602a3 100644
--- a/tests/data/groundtruth/docling_v1/2305.03393v1.json
+++ b/tests/data/groundtruth/docling_v1/2305.03393v1.json
@@ -1 +1 @@
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Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [163.11109924316406, 294.2145080566406, 452.2415771484375, 313.3060607910156], "page": 1, "span": [0, 90], "__ref_s3_data": null}], "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76512145996094, 259.3119201660156, 228.933837890625, 269.88031005859375], "page": 1, "span": [0, 14], "__ref_s3_data": null}], "text": "1 Introduction", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76512145996094, 163.18548583984375, 480.595947265625, 243.7134552001953], "page": 1, "span": [0, 500], "__ref_s3_data": null}], "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76512145996094, 127.14546966552734, 480.5958251953125, 159.85244750976562], "page": 1, "span": [0, 235], "__ref_s3_data": null}], "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 139.37193298339844, 698.22900390625], "page": 2, "span": [0, 1], "__ref_s3_data": null}], "text": "2", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.8133544921875, 690.1593017578125, 231.72227478027344, 698.22900390625], "page": 2, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 591.7794189453125, 480.5918884277344, 665.6658325195312], "page": 2, "span": [0, 574], "__ref_s3_data": null}], "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [134.76499938964844, 271.1133117675781, 480.5923156738281, 339.68621826171875], "page": 2, "span": [0, 435], "__ref_s3_data": null}], "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.7650146484375, 127.14530181884766, 480.5948181152344, 267.44927978515625], "page": 2, "span": [0, 911], "__ref_s3_data": null}], "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 3, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [475.98431396484375, 690.1593017578125, 480.59124755859375, 698.22900390625], "page": 3, "span": [0, 1], "__ref_s3_data": null}], "text": "3", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 580.5831298828125, 480.5918273925781, 673.0662231445312], "page": 3, "span": [0, 584], "__ref_s3_data": null}], "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 460.7701416015625, 480.5957336425781, 577.1641235351562], "page": 3, "span": [0, 721], "__ref_s3_data": null}], "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 352.9132385253906, 480.5956726074219, 457.35211181640625], "page": 3, "span": [0, 626], "__ref_s3_data": null}], "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 319.3436584472656, 236.76913452148438, 329.91204833984375], "page": 3, "span": [0, 14], "__ref_s3_data": null}], "text": "2 Related Work", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 127.14423370361328, 484.1204833984375, 303.3141784667969], "page": 3, "span": [0, 1161], "__ref_s3_data": null}], "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 139.37193298339844, 698.22900390625], "page": 4, "span": [0, 1], "__ref_s3_data": null}], "text": "4", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.8133544921875, 690.1593017578125, 231.72227478027344, 698.22900390625], "page": 4, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 532.7620849609375, 480.59576416015625, 673.0662231445312], "page": 4, "span": [0, 939], "__ref_s3_data": null}], "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 305.3533020019531, 480.595703125, 529.3430786132812], "page": 4, "span": [0, 1404], "__ref_s3_data": null}], "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 209.4513397216797, 480.5937805175781, 301.93426513671875], "page": 4, "span": [0, 572], "__ref_s3_data": null}], "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 175.88177490234375, 269.6244201660156, 186.45016479492188], "page": 4, "span": [0, 19], "__ref_s3_data": null}], "text": "3 Problem Statement", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 127.14434051513672, 480.59368896484375, 159.85231018066406], "page": 4, "span": [0, 233], "__ref_s3_data": null}], "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 5, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [475.98431396484375, 690.1593017578125, 480.59124755859375, 698.22900390625], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "5", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 604.4931640625, 480.5937805175781, 673.0662231445312], "page": 5, "span": [0, 422], "__ref_s3_data": null}], "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [145.6070098876953, 562.7882080078125, 469.7522277832031, 570.9207153320312], "page": 5, "span": [0, 73], "__ref_s3_data": null}], "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"prov": [{"bbox": [134.76499938964844, 259.57940673828125, 480.5947570800781, 423.793212890625], "page": 5, "span": [0, 1021], "__ref_s3_data": null}], "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.7650146484375, 211.29440307617188, 480.5928955078125, 255.95736694335938], "page": 5, "span": [0, 313], "__ref_s3_data": null}], "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.7650146484375, 127.14539337158203, 480.5947265625, 207.67337036132812], "page": 5, "span": [0, 542], "__ref_s3_data": null}], "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 139.37193298339844, 698.22900390625], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "6", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.8133544921875, 690.1593017578125, 231.72227478027344, 698.22900390625], "page": 6, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 652.314208984375, 480.59478759765625, 673.0662231445312], "page": 6, "span": [0, 132], "__ref_s3_data": null}], "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 496.2580871582031, 480.595703125, 648.5172119140625], "page": 6, "span": [0, 977], "__ref_s3_data": null}], "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 460.8005065917969, 372.50848388671875, 471.368896484375], "page": 6, "span": [0, 36], "__ref_s3_data": null}], "text": "4 Optimised Table Structure Language", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 350.400146484375, 480.5947265625, 442.8830261230469], "page": 6, "span": [0, 563], "__ref_s3_data": null}], "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 317.3211364746094, 261.80108642578125, 326.1280822753906], "page": 6, "span": [0, 23], "__ref_s3_data": null}], "text": "4.1 Language Definition", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 270.2941589355469, 480.5887145996094, 303.0021057128906], "page": 6, "span": [0, 165], "__ref_s3_data": null}], "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [149.708984375, 257.701171875, 409.3113708496094, 266.4981384277344], "page": 6, "span": [0, 57], "__ref_s3_data": null}], "text": "The OTSL vocabulary is comprised of the following tokens:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [140.99298095703125, 235.22317504882812, 460.54443359375, 244.0301055908203], "page": 6, "span": [0, 72], "__ref_s3_data": null}], "text": "- -\"C\" cell a new table cell that either has or does not have cell content", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.99301147460938, 210.6751708984375, 480.59393310546875, 231.43710327148438], "page": 6, "span": [0, 82], "__ref_s3_data": null}], "text": "- -\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.9930419921875, 186.1261749267578, 480.58856201171875, 206.8881072998047], "page": 6, "span": [0, 81], "__ref_s3_data": null}], "text": "- -\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.9930419921875, 173.53317260742188, 454.5549621582031, 182.34010314941406], "page": 6, "span": [0, 71], "__ref_s3_data": null}], "text": "- -\"X\" cell cross cell , to merge with both left and upper neighbor cells", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.9930419921875, 160.93917846679688, 328.61676025390625, 169.74610900878906], "page": 6, "span": [0, 40], "__ref_s3_data": null}], "text": "- -\"NL\" new-line , switch to the next row.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76504516601562, 127.14515686035156, 480.5928039550781, 147.8971405029297], "page": 6, "span": [0, 99], "__ref_s3_data": null}], "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 7, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [475.98431396484375, 690.1593017578125, 480.59124755859375, 698.22900390625], "page": 7, "span": [0, 1], "__ref_s3_data": null}], "text": "7", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 636.1503295898438, 480.58740234375, 666.2008056640625], "page": 7, "span": [0, 207], "__ref_s3_data": null}], "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"prov": [{"bbox": [134.76499938964844, 477.8972473144531, 246.6519775390625, 486.7041931152344], "page": 7, "span": [0, 19], "__ref_s3_data": null}], "text": "4.2 Language Syntax", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 457.95526123046875, 363.7961730957031, 466.7522277832031], "page": 7, "span": [0, 51], "__ref_s3_data": null}], "text": "The OTSL representation follows these syntax rules:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [138.97299194335938, 424.0662536621094, 480.5890197753906, 444.8291931152344], "page": 7, "span": [0, 108], "__ref_s3_data": null}], "text": "- 1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 400.15325927734375, 480.59228515625, 420.9151916503906], "page": 7, "span": [0, 106], "__ref_s3_data": null}], "text": "- 2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 388.19525146484375, 226.0736083984375, 397.002197265625], "page": 7, "span": [0, 20], "__ref_s3_data": null}], "text": "3. Cross cell rule :", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [151.70098876953125, 352.3262939453125, 480.5923767089844, 385.0332336425781], "page": 7, "span": [0, 167], "__ref_s3_data": null}], "text": "- The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 340.3673095703125, 474.5901794433594, 349.17425537109375], "page": 7, "span": [0, 78], "__ref_s3_data": null}], "text": "- 4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 316.4543151855469, 480.58746337890625, 337.21624755859375], "page": 7, "span": [0, 84], "__ref_s3_data": null}], "text": "- 5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 292.5403137207031, 480.5945739746094, 313.3032531738281], "page": 7, "span": [0, 144], "__ref_s3_data": null}], "text": "- 6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76498413085938, 151.05833435058594, 480.5958251953125, 279.40728759765625], "page": 7, "span": [0, 848], "__ref_s3_data": null}], "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 127.14533233642578, 480.5926513671875, 147.89730834960938], "page": 7, "span": [0, 153], "__ref_s3_data": null}], "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 139.37193298339844, 698.22900390625], "page": 8, "span": [0, 1], "__ref_s3_data": null}], "text": "8", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.8133544921875, 690.1593017578125, 231.72227478027344, 698.22900390625], "page": 8, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 652.314208984375, 480.5888366699219, 673.0662231445312], "page": 8, "span": [0, 84], "__ref_s3_data": null}], "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 621.63623046875, 319.3470764160156, 630.4431762695312], "page": 8, "span": [0, 35], "__ref_s3_data": null}], "text": "4.3 Error-detection and -mitigation", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 493.32415771484375, 480.59576416015625, 609.7182006835938], "page": 8, "span": [0, 797], "__ref_s3_data": null}], "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 460.2676086425781, 229.03533935546875, 470.83599853515625], "page": 8, "span": [0, 13], "__ref_s3_data": null}], "text": "5 Experiments", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 340.3122863769531, 480.59527587890625, 444.7501525878906], "page": 8, "span": [0, 684], "__ref_s3_data": null}], "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.7650146484375, 288.2603454589844, 480.5908203125, 307.35186767578125], "page": 8, "span": [0, 104], "__ref_s3_data": null}], "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [134.76499938964844, 127.1452407836914, 480.59173583984375, 171.80722045898438], "page": 8, "span": [0, 299], "__ref_s3_data": null}], "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 9, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [475.98431396484375, 690.1593017578125, 480.59124755859375, 698.22900390625], "page": 9, "span": [0, 1], "__ref_s3_data": null}], "text": "9", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 640.3582153320312, 480.5957946777344, 673.0662231445312], "page": 9, "span": [0, 163], "__ref_s3_data": null}], "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 614.0072021484375, 318.44842529296875, 622.8141479492188], "page": 9, "span": [0, 32], "__ref_s3_data": null}], "text": "5.1 Hyper Parameter Optimization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 537.8411254882812, 480.5927734375, 606.4141845703125], "page": 9, "span": [0, 423], "__ref_s3_data": null}], "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 464.9591979980469, 480.59539794921875, 516.9276733398438], "page": 9, "span": [0, 398], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [134.76499938964844, 275.041259765625, 264.4033203125, 283.84820556640625], "page": 9, "span": [0, 24], "__ref_s3_data": null}], "text": "5.2 Quantitative Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 174.9652557373047, 480.59576416015625, 267.44921875], "page": 9, "span": [0, 555], "__ref_s3_data": null}], "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 127.1452407836914, 480.59576416015625, 171.80722045898438], "page": 9, "span": [0, 289], "__ref_s3_data": null}], "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 143.97886657714844, 698.22900390625], "page": 10, "span": [0, 2], "__ref_s3_data": null}], "text": "10", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.82052612304688, 690.1593017578125, 231.72048950195312, 698.22900390625], "page": 10, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 646.1133422851562, 480.59356689453125, 676.163818359375], "page": 10, "span": [0, 192], "__ref_s3_data": null}], "text": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [134.76499938964844, 494.27825927734375, 257.0867919921875, 503.085205078125], "page": 10, "span": [0, 23], "__ref_s3_data": null}], "text": "5.3 Qualitative Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 425.5223083496094, 480.5898132324219, 482.13922119140625], "page": 10, "span": [0, 309], "__ref_s3_data": null}], "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 352.2828369140625, 480.591064453125, 394.4098815917969], "page": 10, "span": [0, 270], "__ref_s3_data": null}], "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [227.91465759277344, 116.65360260009766, 230.10028076171875, 126.1739730834961], "page": 10, "span": [0, 1], "__ref_s3_data": null}], "text": "\u03bc", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [300.58056640625, 98.57134246826172, 302.72637939453125, 108.3780517578125], "page": 10, "span": [0, 1], "__ref_s3_data": null}], "text": "\u2265", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 11, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [471.3756103515625, 690.1593017578125, 480.5894775390625, 698.22900390625], "page": 11, "span": [0, 2], "__ref_s3_data": null}], "text": "11", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 614.2323608398438, 480.58837890625, 666.2008056640625], "page": 11, "span": [0, 390], "__ref_s3_data": null}], "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 143.97886657714844, 698.22900390625], "page": 12, "span": [0, 2], "__ref_s3_data": null}], "text": "12", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.82052612304688, 690.1593017578125, 231.72048950195312, 698.22900390625], "page": 12, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 663.8826293945312, 219.25479125976562, 674.4510498046875], "page": 12, "span": [0, 12], "__ref_s3_data": null}], "text": "6 Conclusion", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 588.5181884765625, 480.595703125, 645.13623046875], "page": 12, "span": [0, 330], "__ref_s3_data": null}], "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 468.1632080078125, 480.59478759765625, 584.5562133789062], "page": 12, "span": [0, 724], "__ref_s3_data": null}], "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 323.8973388671875, 480.5948181152344, 464.201171875], "page": 12, "span": [0, 926], "__ref_s3_data": null}], "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 287.61077880859375, 197.68641662597656, 298.1791687011719], "page": 12, "span": [0, 10], "__ref_s3_data": null}], "text": "References", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [139.37100219726562, 228.12855529785156, 480.5920104980469, 269.1201477050781], "page": 12, "span": [0, 270], "__ref_s3_data": null}], "text": "- 1. 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Springer International Publishing, Cham (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.3709716796875, 160.81239318847656, 480.5873107910156, 179.84115600585938], "page": 12, "span": [0, 140], "__ref_s3_data": null}], "text": "- 3. Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.3709716796875, 127.13239288330078, 480.5882568359375, 157.11915588378906], "page": 12, "span": [0, 204], "__ref_s3_data": null}], "text": "- 4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 13, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [471.3756103515625, 690.1593017578125, 480.5894775390625, 698.22900390625], "page": 13, "span": [0, 2], "__ref_s3_data": null}], "text": "13", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [139.37100219726562, 642.3383178710938, 480.59478759765625, 672.3259887695312], "page": 13, "span": [0, 203], "__ref_s3_data": null}], "text": "- 5. Kayal, P., Anand, M., Desai, H., Singh, M.: Tables to latex: structure and content extraction from scientific tables. International Journal on Document Analysis and Recognition (IJDAR) pp. 1-10 (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.37100219726562, 598.4913940429688, 480.5928649902344, 639.4380493164062], "page": 13, "span": [0, 264], "__ref_s3_data": null}], "text": "- 6. Lee, E., Kwon, J., Yang, H., Park, J., Lee, S., Koo, H.I., Cho, N.I.: Table structure recognition based on grid shape graph. In: 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). pp. 18681873. IEEE (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.37100219726562, 576.5624389648438, 480.5901184082031, 595.5911254882812], "page": 13, "span": [0, 131], "__ref_s3_data": null}], "text": "- 7. Li, M., Cui, L., Huang, S., Wei, F., Zhou, M., Li, Z.: Tablebank: A benchmark dataset for table detection and recognition (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.37100219726562, 521.7116088867188, 480.5947265625, 573.6611328125], "page": 13, "span": [0, 345], "__ref_s3_data": null}], "text": "- 8. Livathinos, N., Berrospi, C., Lysak, M., Kuropiatnyk, V., Nassar, A., Carvalho, A., Dolfi, M., Auer, C., Dinkla, K., Staar, P.: Robust pdf document conversion using recurrent neural networks. Proceedings of the AAAI Conference on Artificial Intelligence 35 (17), 15137-15145 (May 2021), https://ojs.aaai.org/index.php/ AAAI/article/view/17777", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.37100219726562, 488.8674621582031, 480.5938720703125, 518.8551635742188], "page": 13, "span": [0, 234], "__ref_s3_data": null}], "text": "- 9. Nassar, A., Livathinos, N., Lysak, M., Staar, P.: Tableformer: Table structure understanding with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4614-4623 (June 2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 423.05767822265625, 480.5937194824219, 485.96722412109375], "page": 13, "span": [0, 413], "__ref_s3_data": null}], "text": "- 10. Pfitzmann, B., Auer, C., Dolfi, M., Nassar, A.S., Staar, P.W.J.: Doclaynet: A large human-annotated dataset for document-layout segmentation. In: Zhang, A., Rangwala, H. (eds.) KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022. pp. 3743-3751. ACM (2022). https://doi.org/10.1145/3534678.3539043 , https:// doi.org/10.1145/3534678.3539043", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 379.2555236816406, 480.59295654296875, 420.2022705078125], "page": 13, "span": [0, 295], "__ref_s3_data": null}], "text": "- 11. Prasad, D., Gadpal, A., Kapadni, K., Visave, M., Sultanpure, K.: Cascadetabnet: An approach for end to end table detection and structure recognition from imagebased documents. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 572-573 (2020)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 335.4085388183594, 480.5946960449219, 376.35528564453125], "page": 13, "span": [0, 281], "__ref_s3_data": null}], "text": "- 12. Schreiber, S., Agne, S., Wolf, I., Dengel, A., Ahmed, S.: Deepdesrt: Deep learning for detection and structure recognition of tables in document images. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR). vol. 1, pp. 1162-1167. IEEE (2017)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 291.5167236328125, 480.5937194824219, 332.50830078125], "page": 13, "span": [0, 275], "__ref_s3_data": null}], "text": "- 13. Siddiqui, S.A., Fateh, I.A., Rizvi, S.T.R., Dengel, A., Ahmed, S.: Deeptabstr: Deep learning based table structure recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1403-1409 (2019). https:// doi.org/10.1109/ICDAR.2019.00226", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 247.7145538330078, 480.5928649902344, 288.66131591796875], "page": 13, "span": [0, 241], "__ref_s3_data": null}], "text": "- 14. Smock, B., Pesala, R., Abraham, R.: PubTables-1M: Towards comprehensive table extraction from unstructured documents. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4634-4642 (June 2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 181.90472412109375, 480.5958251953125, 244.81431579589844], "page": 13, "span": [0, 405], "__ref_s3_data": null}], "text": "- 15. Staar, P.W.J., Dolfi, M., Auer, C., Bekas, C.: Corpus conversion service: A machine learning platform to ingest documents at scale. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 774-782. KDD '18, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3219819.3219834 , https://doi.org/10. 1145/3219819.3219834", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 160.0205535888672, 480.5954284667969, 179.04931640625], "page": 13, "span": [0, 96], "__ref_s3_data": null}], "text": "- 16. Wang, X.: Tabular Abstraction, Editing, and Formatting. Ph.D. thesis, CAN (1996), aAINN09397", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 127.13255310058594, 480.5911865234375, 157.1203155517578], "page": 13, "span": [0, 195], "__ref_s3_data": null}], "text": "- 17. Xue, W., Li, Q., Tao, D.: Res2tim: Reconstruct syntactic structures from table images. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 749-755. IEEE (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 143.97886657714844, 698.22900390625], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "14", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.82052612304688, 690.1593017578125, 231.72048950195312, 698.22900390625], "page": 14, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 642.3383178710938, 480.59112548828125, 672.3259887695312], "page": 14, "span": [0, 223], "__ref_s3_data": null}], "text": "- 18. Xue, W., Yu, B., Wang, W., Tao, D., Li, Q.: Tgrnet: A table graph reconstruction network for table structure recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1295-1304 (2021)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76499938964844, 598.45751953125, 480.5946960449219, 639.4490356445312], "page": 14, "span": [0, 269], "__ref_s3_data": null}], "text": "- 19. Ye, J., Qi, X., He, Y., Chen, Y., Gu, D., Gao, P., Xiao, R.: Pingan-vcgroup's solution for icdar 2021 competition on scientific literature parsing task b: Table recognition to html (2021). https://doi.org/10.48550/ARXIV.2105.01848 , https://arxiv.org/abs/2105.01848", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.7649688720703, 576.5853881835938, 480.5935363769531, 595.6130981445312], "page": 14, "span": [0, 147], "__ref_s3_data": null}], "text": "- 20. Zhang, Z., Zhang, J., Du, J., Wang, F.: Split, embed and merge: An accurate table structure recognizer. Pattern Recognition 126 , 108565 (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76495361328125, 521.74560546875, 480.5930480957031, 573.6961059570312], "page": 14, "span": [0, 329], "__ref_s3_data": null}], "text": "- 21. Zheng, X., Burdick, D., Popa, L., Zhong, X., Wang, N.X.R.: Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 697-706 (2021). https://doi.org/10.1109/WACV48630.2021. 00074", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76495361328125, 477.9544982910156, 480.5955810546875, 518.9011840820312], "page": 14, "span": [0, 259], "__ref_s3_data": null}], "text": "- 22. Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition: Data, model, and evaluation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision - ECCV 2020. pp. 564-580. Springer International Publishing, Cham (2020)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76495361328125, 445.0785217285156, 480.59454345703125, 475.0652770996094], "page": 14, "span": [0, 206], "__ref_s3_data": null}], "text": "- 23. Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1015-1022. IEEE (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}], "figures": [{"prov": [{"bbox": [148.45362854003906, 366.1531677246094, 464.3610534667969, 583.6259155273438], "page": 2, "span": [0, 574], "__ref_s3_data": null}], "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [137.41452026367188, 451.7695007324219, 476.5608215332031, 558.4876708984375], "page": 5, "span": [0, 73], "__ref_s3_data": null}], "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [164.6502227783203, 511.6589660644531, 449.55072021484375, 628.202880859375], "page": 7, "span": [0, 207], "__ref_s3_data": null}], "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [140.7096710205078, 198.32275390625, 472.73382568359375, 283.93609619140625], "page": 8, "span": [0, 104], "__ref_s3_data": null}], "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [162.67306518554688, 128.786376953125, 451.70025634765625, 347.37744140625], "page": 10, "span": [0, 270], "__ref_s3_data": null}], "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [168.39263916015625, 157.996826171875, 447.35272216796875, 610.0320434570312], "page": 11, "span": [0, 390], "__ref_s3_data": null}], "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [144.5919952392578, 324.0382995605469, 470.76055908203125, 450.2650451660156], "page": 9, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "table", "payload": null, "#-cols": 8, "#-rows": 7, "data": [[{"bbox": [160.3699951171875, 442.1952819824219, 168.0479278564453, 450.2650451660156], "spans": [[0, 0]], "text": "#", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [207.9739990234375, 442.1952819824219, 215.6519317626953, 450.2650451660156], "spans": [[0, 1]], "text": "#", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [239.79800415039062, 436.7162780761719, 278.3176574707031, 444.7860412597656], "spans": [[0, 2]], "text": "Language", "type": "col_header", "col": 2, "col-header": true, "col-span": 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Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [163.11109924316406, 294.2145080566406, 452.2415771484375, 313.3060607910156], "page": 1, "span": [0, 90], "__ref_s3_data": null}], "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76512145996094, 259.3119201660156, 228.933837890625, 269.88031005859375], "page": 1, "span": [0, 14], "__ref_s3_data": null}], "text": "1 Introduction", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76512145996094, 163.18548583984375, 480.595947265625, 243.7134552001953], "page": 1, "span": [0, 500], "__ref_s3_data": null}], "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76512145996094, 127.14546966552734, 480.5958251953125, 159.85244750976562], "page": 1, "span": [0, 235], "__ref_s3_data": null}], "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 139.37193298339844, 698.22900390625], "page": 2, "span": [0, 1], "__ref_s3_data": null}], "text": "2", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.8133544921875, 690.1593017578125, 231.72227478027344, 698.22900390625], "page": 2, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 591.7794189453125, 480.5918884277344, 665.6658325195312], "page": 2, "span": [0, 574], "__ref_s3_data": null}], "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [134.76499938964844, 271.1133117675781, 480.5923156738281, 339.68621826171875], "page": 2, "span": [0, 435], "__ref_s3_data": null}], "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.7650146484375, 127.14530181884766, 480.5948181152344, 267.44927978515625], "page": 2, "span": [0, 911], "__ref_s3_data": null}], "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 3, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [475.98431396484375, 690.1593017578125, 480.59124755859375, 698.22900390625], "page": 3, "span": [0, 1], "__ref_s3_data": null}], "text": "3", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 580.5831298828125, 480.5918273925781, 673.0662231445312], "page": 3, "span": [0, 584], "__ref_s3_data": null}], "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 460.7701416015625, 480.5957336425781, 577.1641235351562], "page": 3, "span": [0, 721], "__ref_s3_data": null}], "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 352.9132385253906, 480.5956726074219, 457.35211181640625], "page": 3, "span": [0, 626], "__ref_s3_data": null}], "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 319.3436584472656, 236.76913452148438, 329.91204833984375], "page": 3, "span": [0, 14], "__ref_s3_data": null}], "text": "2 Related Work", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 127.14423370361328, 484.1204833984375, 303.3141784667969], "page": 3, "span": [0, 1161], "__ref_s3_data": null}], "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 139.37193298339844, 698.22900390625], "page": 4, "span": [0, 1], "__ref_s3_data": null}], "text": "4", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.8133544921875, 690.1593017578125, 231.72227478027344, 698.22900390625], "page": 4, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 532.7620849609375, 480.59576416015625, 673.0662231445312], "page": 4, "span": [0, 939], "__ref_s3_data": null}], "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 305.3533020019531, 480.595703125, 529.3430786132812], "page": 4, "span": [0, 1404], "__ref_s3_data": null}], "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 209.4513397216797, 480.5937805175781, 301.93426513671875], "page": 4, "span": [0, 572], "__ref_s3_data": null}], "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 175.88177490234375, 269.6244201660156, 186.45016479492188], "page": 4, "span": [0, 19], "__ref_s3_data": null}], "text": "3 Problem Statement", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 127.14434051513672, 480.59368896484375, 159.85231018066406], "page": 4, "span": [0, 233], "__ref_s3_data": null}], "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 5, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [475.98431396484375, 690.1593017578125, 480.59124755859375, 698.22900390625], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "5", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 604.4931640625, 480.5937805175781, 673.0662231445312], "page": 5, "span": [0, 422], "__ref_s3_data": null}], "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [145.6070098876953, 562.7882080078125, 469.7522277832031, 570.9207153320312], "page": 5, "span": [0, 73], "__ref_s3_data": null}], "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"prov": [{"bbox": [134.76499938964844, 259.57940673828125, 480.5947570800781, 423.793212890625], "page": 5, "span": [0, 1021], "__ref_s3_data": null}], "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.7650146484375, 211.29440307617188, 480.5928955078125, 255.95736694335938], "page": 5, "span": [0, 313], "__ref_s3_data": null}], "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.7650146484375, 127.14539337158203, 480.5947265625, 207.67337036132812], "page": 5, "span": [0, 542], "__ref_s3_data": null}], "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 139.37193298339844, 698.22900390625], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "6", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.8133544921875, 690.1593017578125, 231.72227478027344, 698.22900390625], "page": 6, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 652.314208984375, 480.59478759765625, 673.0662231445312], "page": 6, "span": [0, 132], "__ref_s3_data": null}], "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 496.2580871582031, 480.595703125, 648.5172119140625], "page": 6, "span": [0, 977], "__ref_s3_data": null}], "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 460.8005065917969, 372.50848388671875, 471.368896484375], "page": 6, "span": [0, 36], "__ref_s3_data": null}], "text": "4 Optimised Table Structure Language", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 350.400146484375, 480.5947265625, 442.8830261230469], "page": 6, "span": [0, 563], "__ref_s3_data": null}], "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 317.3211364746094, 261.80108642578125, 326.1280822753906], "page": 6, "span": [0, 23], "__ref_s3_data": null}], "text": "4.1 Language Definition", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 270.2941589355469, 480.5887145996094, 303.0021057128906], "page": 6, "span": [0, 165], "__ref_s3_data": null}], "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [149.708984375, 257.701171875, 409.3113708496094, 266.4981384277344], "page": 6, "span": [0, 57], "__ref_s3_data": null}], "text": "The OTSL vocabulary is comprised of the following tokens:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [140.99298095703125, 235.22317504882812, 460.54443359375, 244.0301055908203], "page": 6, "span": [0, 72], "__ref_s3_data": null}], "text": "- -\"C\" cell a new table cell that either has or does not have cell content", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.99301147460938, 210.6751708984375, 480.59393310546875, 231.43710327148438], "page": 6, "span": [0, 82], "__ref_s3_data": null}], "text": "- -\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.9930419921875, 186.1261749267578, 480.58856201171875, 206.8881072998047], "page": 6, "span": [0, 81], "__ref_s3_data": null}], "text": "- -\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.9930419921875, 173.53317260742188, 454.5549621582031, 182.34010314941406], "page": 6, "span": [0, 71], "__ref_s3_data": null}], "text": "- -\"X\" cell cross cell , to merge with both left and upper neighbor cells", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.9930419921875, 160.93917846679688, 328.61676025390625, 169.74610900878906], "page": 6, "span": [0, 40], "__ref_s3_data": null}], "text": "- -\"NL\" new-line , switch to the next row.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76504516601562, 127.14515686035156, 480.5928039550781, 147.8971405029297], "page": 6, "span": [0, 99], "__ref_s3_data": null}], "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 7, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [475.98431396484375, 690.1593017578125, 480.59124755859375, 698.22900390625], "page": 7, "span": [0, 1], "__ref_s3_data": null}], "text": "7", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 636.1503295898438, 480.58740234375, 666.2008056640625], "page": 7, "span": [0, 207], "__ref_s3_data": null}], "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"prov": [{"bbox": [134.76499938964844, 477.8972473144531, 246.6519775390625, 486.7041931152344], "page": 7, "span": [0, 19], "__ref_s3_data": null}], "text": "4.2 Language Syntax", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 457.95526123046875, 363.7961730957031, 466.7522277832031], "page": 7, "span": [0, 51], "__ref_s3_data": null}], "text": "The OTSL representation follows these syntax rules:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [138.97299194335938, 424.0662536621094, 480.5890197753906, 444.8291931152344], "page": 7, "span": [0, 108], "__ref_s3_data": null}], "text": "- 1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 400.15325927734375, 480.59228515625, 420.9151916503906], "page": 7, "span": [0, 106], "__ref_s3_data": null}], "text": "- 2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 388.19525146484375, 226.0736083984375, 397.002197265625], "page": 7, "span": [0, 20], "__ref_s3_data": null}], "text": "3. Cross cell rule :", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [151.70098876953125, 352.3262939453125, 480.5923767089844, 385.0332336425781], "page": 7, "span": [0, 167], "__ref_s3_data": null}], "text": "- The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 340.3673095703125, 474.5901794433594, 349.17425537109375], "page": 7, "span": [0, 78], "__ref_s3_data": null}], "text": "- 4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 316.4543151855469, 480.58746337890625, 337.21624755859375], "page": 7, "span": [0, 84], "__ref_s3_data": null}], "text": "- 5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 292.5403137207031, 480.5945739746094, 313.3032531738281], "page": 7, "span": [0, 144], "__ref_s3_data": null}], "text": "- 6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76498413085938, 151.05833435058594, 480.5958251953125, 279.40728759765625], "page": 7, "span": [0, 848], "__ref_s3_data": null}], "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 127.14533233642578, 480.5926513671875, 147.89730834960938], "page": 7, "span": [0, 153], "__ref_s3_data": null}], "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 139.37193298339844, 698.22900390625], "page": 8, "span": [0, 1], "__ref_s3_data": null}], "text": "8", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.8133544921875, 690.1593017578125, 231.72227478027344, 698.22900390625], "page": 8, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 652.314208984375, 480.5888366699219, 673.0662231445312], "page": 8, "span": [0, 84], "__ref_s3_data": null}], "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 621.63623046875, 319.3470764160156, 630.4431762695312], "page": 8, "span": [0, 35], "__ref_s3_data": null}], "text": "4.3 Error-detection and -mitigation", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 493.32415771484375, 480.59576416015625, 609.7182006835938], "page": 8, "span": [0, 797], "__ref_s3_data": null}], "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 460.2676086425781, 229.03533935546875, 470.83599853515625], "page": 8, "span": [0, 13], "__ref_s3_data": null}], "text": "5 Experiments", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 340.3122863769531, 480.59527587890625, 444.7501525878906], "page": 8, "span": [0, 684], "__ref_s3_data": null}], "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.7650146484375, 288.2603454589844, 480.5908203125, 307.35186767578125], "page": 8, "span": [0, 104], "__ref_s3_data": null}], "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [134.76499938964844, 127.1452407836914, 480.59173583984375, 171.80722045898438], "page": 8, "span": [0, 299], "__ref_s3_data": null}], "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 9, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [475.98431396484375, 690.1593017578125, 480.59124755859375, 698.22900390625], "page": 9, "span": [0, 1], "__ref_s3_data": null}], "text": "9", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 640.3582153320312, 480.5957946777344, 673.0662231445312], "page": 9, "span": [0, 163], "__ref_s3_data": null}], "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 614.0072021484375, 318.44842529296875, 622.8141479492188], "page": 9, "span": [0, 32], "__ref_s3_data": null}], "text": "5.1 Hyper Parameter Optimization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 537.8411254882812, 480.5927734375, 606.4141845703125], "page": 9, "span": [0, 423], "__ref_s3_data": null}], "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 464.9591979980469, 480.59539794921875, 516.9276733398438], "page": 9, "span": [0, 398], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [134.76499938964844, 275.041259765625, 264.4033203125, 283.84820556640625], "page": 9, "span": [0, 24], "__ref_s3_data": null}], "text": "5.2 Quantitative Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 174.9652557373047, 480.59576416015625, 267.44921875], "page": 9, "span": [0, 555], "__ref_s3_data": null}], "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 127.1452407836914, 480.59576416015625, 171.80722045898438], "page": 9, "span": [0, 289], "__ref_s3_data": null}], "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 143.97886657714844, 698.22900390625], "page": 10, "span": [0, 2], "__ref_s3_data": null}], "text": "10", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.82052612304688, 690.1593017578125, 231.72048950195312, 698.22900390625], "page": 10, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 646.1133422851562, 480.59356689453125, 676.163818359375], "page": 10, "span": [0, 192], "__ref_s3_data": null}], "text": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [134.76499938964844, 494.27825927734375, 257.0867919921875, 503.085205078125], "page": 10, "span": [0, 23], "__ref_s3_data": null}], "text": "5.3 Qualitative Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 425.5223083496094, 480.5898132324219, 482.13922119140625], "page": 10, "span": [0, 309], "__ref_s3_data": null}], "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 352.2828369140625, 480.591064453125, 394.4098815917969], "page": 10, "span": [0, 270], "__ref_s3_data": null}], "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [227.91465759277344, 116.65360260009766, 230.10028076171875, 126.1739730834961], "page": 10, "span": [0, 1], "__ref_s3_data": null}], "text": "\u03bc", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [300.58056640625, 98.57134246826172, 302.72637939453125, 108.3780517578125], "page": 10, "span": [0, 1], "__ref_s3_data": null}], "text": "\u2265", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 11, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [471.3756103515625, 690.1593017578125, 480.5894775390625, 698.22900390625], "page": 11, "span": [0, 2], "__ref_s3_data": null}], "text": "11", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 614.2323608398438, 480.58837890625, 666.2008056640625], "page": 11, "span": [0, 390], "__ref_s3_data": null}], "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 143.97886657714844, 698.22900390625], "page": 12, "span": [0, 2], "__ref_s3_data": null}], "text": "12", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.82052612304688, 690.1593017578125, 231.72048950195312, 698.22900390625], "page": 12, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 663.8826293945312, 219.25479125976562, 674.4510498046875], "page": 12, "span": [0, 12], "__ref_s3_data": null}], "text": "6 Conclusion", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 588.5181884765625, 480.595703125, 645.13623046875], "page": 12, "span": [0, 330], "__ref_s3_data": null}], "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 468.1632080078125, 480.59478759765625, 584.5562133789062], "page": 12, "span": [0, 724], "__ref_s3_data": null}], "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 323.8973388671875, 480.5948181152344, 464.201171875], "page": 12, "span": [0, 926], "__ref_s3_data": null}], "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 287.61077880859375, 197.68641662597656, 298.1791687011719], "page": 12, "span": [0, 10], "__ref_s3_data": null}], "text": "References", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [139.37100219726562, 228.12855529785156, 480.5920104980469, 269.1201477050781], "page": 12, "span": [0, 270], "__ref_s3_data": null}], "text": "- 1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.3709716796875, 183.53439331054688, 480.5920104980469, 224.4811553955078], "page": 12, "span": [0, 301], "__ref_s3_data": null}], "text": "- 2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545561. Springer International Publishing, Cham (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.3709716796875, 160.81239318847656, 480.5873107910156, 179.84115600585938], "page": 12, "span": [0, 140], "__ref_s3_data": null}], "text": "- 3. Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.3709716796875, 127.13239288330078, 480.5882568359375, 157.11915588378906], "page": 12, "span": [0, 204], "__ref_s3_data": null}], "text": "- 4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 13, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [471.3756103515625, 690.1593017578125, 480.5894775390625, 698.22900390625], "page": 13, "span": [0, 2], "__ref_s3_data": null}], "text": "13", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [139.37100219726562, 642.3383178710938, 480.59478759765625, 672.3259887695312], "page": 13, "span": [0, 203], "__ref_s3_data": null}], "text": "- 5. Kayal, P., Anand, M., Desai, H., Singh, M.: Tables to latex: structure and content extraction from scientific tables. International Journal on Document Analysis and Recognition (IJDAR) pp. 1-10 (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.37100219726562, 598.4913940429688, 480.5928649902344, 639.4380493164062], "page": 13, "span": [0, 264], "__ref_s3_data": null}], "text": "- 6. Lee, E., Kwon, J., Yang, H., Park, J., Lee, S., Koo, H.I., Cho, N.I.: Table structure recognition based on grid shape graph. In: 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). pp. 18681873. IEEE (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.37100219726562, 576.5624389648438, 480.5901184082031, 595.5911254882812], "page": 13, "span": [0, 131], "__ref_s3_data": null}], "text": "- 7. Li, M., Cui, L., Huang, S., Wei, F., Zhou, M., Li, Z.: Tablebank: A benchmark dataset for table detection and recognition (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.37100219726562, 521.7116088867188, 480.5947265625, 573.6611328125], "page": 13, "span": [0, 345], "__ref_s3_data": null}], "text": "- 8. Livathinos, N., Berrospi, C., Lysak, M., Kuropiatnyk, V., Nassar, A., Carvalho, A., Dolfi, M., Auer, C., Dinkla, K., Staar, P.: Robust pdf document conversion using recurrent neural networks. Proceedings of the AAAI Conference on Artificial Intelligence 35 (17), 15137-15145 (May 2021), https://ojs.aaai.org/index.php/ AAAI/article/view/17777", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.37100219726562, 488.8674621582031, 480.5938720703125, 518.8551635742188], "page": 13, "span": [0, 234], "__ref_s3_data": null}], "text": "- 9. Nassar, A., Livathinos, N., Lysak, M., Staar, P.: Tableformer: Table structure understanding with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4614-4623 (June 2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 423.05767822265625, 480.5937194824219, 485.96722412109375], "page": 13, "span": [0, 413], "__ref_s3_data": null}], "text": "- 10. Pfitzmann, B., Auer, C., Dolfi, M., Nassar, A.S., Staar, P.W.J.: Doclaynet: A large human-annotated dataset for document-layout segmentation. In: Zhang, A., Rangwala, H. (eds.) KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022. pp. 3743-3751. ACM (2022). https://doi.org/10.1145/3534678.3539043 , https:// doi.org/10.1145/3534678.3539043", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 379.2555236816406, 480.59295654296875, 420.2022705078125], "page": 13, "span": [0, 295], "__ref_s3_data": null}], "text": "- 11. Prasad, D., Gadpal, A., Kapadni, K., Visave, M., Sultanpure, K.: Cascadetabnet: An approach for end to end table detection and structure recognition from imagebased documents. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 572-573 (2020)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 335.4085388183594, 480.5946960449219, 376.35528564453125], "page": 13, "span": [0, 281], "__ref_s3_data": null}], "text": "- 12. Schreiber, S., Agne, S., Wolf, I., Dengel, A., Ahmed, S.: Deepdesrt: Deep learning for detection and structure recognition of tables in document images. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR). vol. 1, pp. 1162-1167. IEEE (2017)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 291.5167236328125, 480.5937194824219, 332.50830078125], "page": 13, "span": [0, 275], "__ref_s3_data": null}], "text": "- 13. Siddiqui, S.A., Fateh, I.A., Rizvi, S.T.R., Dengel, A., Ahmed, S.: Deeptabstr: Deep learning based table structure recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1403-1409 (2019). https:// doi.org/10.1109/ICDAR.2019.00226", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 247.7145538330078, 480.5928649902344, 288.66131591796875], "page": 13, "span": [0, 241], "__ref_s3_data": null}], "text": "- 14. Smock, B., Pesala, R., Abraham, R.: PubTables-1M: Towards comprehensive table extraction from unstructured documents. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4634-4642 (June 2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 181.90472412109375, 480.5958251953125, 244.81431579589844], "page": 13, "span": [0, 405], "__ref_s3_data": null}], "text": "- 15. Staar, P.W.J., Dolfi, M., Auer, C., Bekas, C.: Corpus conversion service: A machine learning platform to ingest documents at scale. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 774-782. KDD '18, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3219819.3219834 , https://doi.org/10. 1145/3219819.3219834", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 160.0205535888672, 480.5954284667969, 179.04931640625], "page": 13, "span": [0, 96], "__ref_s3_data": null}], "text": "- 16. Wang, X.: Tabular Abstraction, Editing, and Formatting. Ph.D. thesis, CAN (1996), aAINN09397", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 127.13255310058594, 480.5911865234375, 157.1203155517578], "page": 13, "span": [0, 195], "__ref_s3_data": null}], "text": "- 17. Xue, W., Li, Q., Tao, D.: Res2tim: Reconstruct syntactic structures from table images. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 749-755. IEEE (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 143.97886657714844, 698.22900390625], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "14", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.82052612304688, 690.1593017578125, 231.72048950195312, 698.22900390625], "page": 14, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 642.3383178710938, 480.59112548828125, 672.3259887695312], "page": 14, "span": [0, 223], "__ref_s3_data": null}], "text": "- 18. Xue, W., Yu, B., Wang, W., Tao, D., Li, Q.: Tgrnet: A table graph reconstruction network for table structure recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1295-1304 (2021)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76499938964844, 598.45751953125, 480.5946960449219, 639.4490356445312], "page": 14, "span": [0, 269], "__ref_s3_data": null}], "text": "- 19. Ye, J., Qi, X., He, Y., Chen, Y., Gu, D., Gao, P., Xiao, R.: Pingan-vcgroup's solution for icdar 2021 competition on scientific literature parsing task b: Table recognition to html (2021). https://doi.org/10.48550/ARXIV.2105.01848 , https://arxiv.org/abs/2105.01848", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.7649688720703, 576.5853881835938, 480.5935363769531, 595.6130981445312], "page": 14, "span": [0, 147], "__ref_s3_data": null}], "text": "- 20. Zhang, Z., Zhang, J., Du, J., Wang, F.: Split, embed and merge: An accurate table structure recognizer. Pattern Recognition 126 , 108565 (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76495361328125, 521.74560546875, 480.5930480957031, 573.6961059570312], "page": 14, "span": [0, 329], "__ref_s3_data": null}], "text": "- 21. Zheng, X., Burdick, D., Popa, L., Zhong, X., Wang, N.X.R.: Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 697-706 (2021). https://doi.org/10.1109/WACV48630.2021. 00074", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76495361328125, 477.9544982910156, 480.5955810546875, 518.9011840820312], "page": 14, "span": [0, 259], "__ref_s3_data": null}], "text": "- 22. Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition: Data, model, and evaluation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision - ECCV 2020. pp. 564-580. Springer International Publishing, Cham (2020)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76495361328125, 445.0785217285156, 480.59454345703125, 475.0652770996094], "page": 14, "span": [0, 206], "__ref_s3_data": null}], "text": "- 23. Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1015-1022. IEEE (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}], "figures": [{"prov": [{"bbox": [148.45362854003906, 366.1531677246094, 464.3610534667969, 583.6259155273438], "page": 2, "span": [0, 574], "__ref_s3_data": null}], "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [137.41452026367188, 451.7695007324219, 476.5608215332031, 558.4876708984375], "page": 5, "span": [0, 73], "__ref_s3_data": null}], "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [164.6502227783203, 511.6589660644531, 449.55072021484375, 628.202880859375], "page": 7, "span": [0, 207], "__ref_s3_data": null}], "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [140.7096710205078, 198.32275390625, 472.73382568359375, 283.93609619140625], "page": 8, "span": [0, 104], "__ref_s3_data": null}], "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [162.67306518554688, 128.786376953125, 451.70025634765625, 347.37744140625], "page": 10, "span": [0, 270], "__ref_s3_data": null}], "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [168.39263916015625, 157.996826171875, 447.35272216796875, 610.0320434570312], "page": 11, "span": [0, 390], "__ref_s3_data": null}], "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [139.66845703125, 322.5279235839844, 475.00372314453125, 454.4252624511719], "page": 9, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. 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diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1.md b/tests/data/groundtruth/docling_v1/2305.03393v1.md
index e0c77bb3..08bed1eb 100644
--- a/tests/data/groundtruth/docling_v1/2305.03393v1.md
+++ b/tests/data/groundtruth/docling_v1/2305.03393v1.md
@@ -130,12 +130,12 @@ Table 1. HPO performed in OTSL and HTML representation on the same transformer-b
 
 | #          | #          | Language   | TEDs        | TEDs        | TEDs        | mAP         | Inference   |
 |------------|------------|------------|-------------|-------------|-------------|-------------|-------------|
-| enc-layers | dec-layers |            | simple      | complex     | all         | (0.75)      | time (secs) |
+| enc-layers | dec-layers | Language   | simple      | complex     | all         | (0.75)      | time (secs) |
 | 6          | 6          | OTSL HTML  | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857  | 2.73 5.39   |
-| 4          | 4          | OTSL       | 0.938       | 0.904       | 0.927       | 0.853       | 1.97        |
-|            |            | HTML       | 0.952       | 0.909       | 0.938       | 0.843       | 3.77        |
-| 2          | 4          | OTSL HTML  | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91        |
-| 4          | 2          | OTSL HTML  | 0.952 0.944 | 0.92 0.903  | 0.942 0.931 | 0.857 0.824 | 3.81 1.22 2 |
+| 4          | 4          | OTSL HTML  | 0.938 0.952 | 0.904       | 0.927       | 0.853       | 1.97        |
+| 2          | 4          | OTSL       | 0.923 0.945 | 0.909 0.897 | 0.938       | 0.843       | 3.77        |
+|            |            | HTML       |             | 0.901       | 0.915 0.931 | 0.859 0.834 | 1.91 3.81   |
+| 4          | 2          | OTSL HTML  | 0.952 0.944 | 0.92 0.903  | 0.942 0.931 | 0.857 0.824 | 1.22 2      |
 
 ## 5.2 Quantitative Results
 
@@ -145,15 +145,15 @@ Additionally, the results show that OTSL has an advantage over HTML when applied
 
 Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).
 
-|              | Language   | TEDs   | TEDs    | TEDs   | mAP(0.75)   | Inference   |
-|--------------|------------|--------|---------|--------|-------------|-------------|
-| Data set     |            | simple | complex | all    |             | time (secs) |
-| PubTabNet    | OTSL       | 0.965  | 0.934   | 0.955  | 0.88        | 2.73        |
-| PubTabNet    | HTML       | 0.969  | 0.927   | 0.955  | 0.857       | 5.39        |
-| FinTabNet    | OTSL       | 0.955  | 0.961   | 0.959  | 0.862       | 1.85        |
-| FinTabNet    | HTML       | 0.917  | 0.922   | 0.92   | 0.722       | 3.26        |
-| PubTables-1M | OTSL       | 0.987  | 0.964   | 0.977  | 0.896       | 1.79        |
-| PubTables-1M | HTML       | 0.983  | 0.944   | 0.966  | 0.889       | 3.26        |
+|              | Language   | TEDs   | TEDs    | TEDs   | mAP(0.75)   | Inference time (secs)   |
+|--------------|------------|--------|---------|--------|-------------|-------------------------|
+|              | Language   | simple | complex | all    | mAP(0.75)   | Inference time (secs)   |
+| PubTabNet    | OTSL       | 0.965  | 0.934   | 0.955  | 0.88        | 2.73                    |
+| PubTabNet    | HTML       | 0.969  | 0.927   | 0.955  | 0.857       | 5.39                    |
+| FinTabNet    | OTSL       | 0.955  | 0.961   | 0.959  | 0.862       | 1.85                    |
+| FinTabNet    | HTML       | 0.917  | 0.922   | 0.92   | 0.722       | 3.26                    |
+| PubTables-1M | OTSL       | 0.987  | 0.964   | 0.977  | 0.896       | 1.79                    |
+| PubTables-1M | HTML       | 0.983  | 0.944   | 0.966  | 0.889       | 3.26                    |
 
 ## 5.3 Qualitative Results
 
diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1.pages.json b/tests/data/groundtruth/docling_v1/2305.03393v1.pages.json
index 48a6ba8f..88df3ac7 100644
--- a/tests/data/groundtruth/docling_v1/2305.03393v1.pages.json
+++ b/tests/data/groundtruth/docling_v1/2305.03393v1.pages.json
@@ -1 +1 @@
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"coord_origin": "TOPLEFT"}}, {"id": 39, "text": "{mly,ahn,nli,cau,taa}@zurich.ibm.com", "bbox": {"l": 222.96609, "t": 228.80853000000002, "r": 392.38983, "b": 236.27752999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Abstract.", "bbox": {"l": 163.1111, "t": 270.30115, "r": 206.6358, "b": 278.22748, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Extracting tables from documents is a crucial task in any", "bbox": {"l": 211.6171, "t": 270.36395000000005, "r": 452.2447199999999, "b": 278.43364999999994, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "document conversion pipeline. Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. 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Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.93040531873703, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 115.89910999999995, "r": 480.59735, "b": 146.51409999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.89183509349823, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure", "bbox": {"l": 134.765, "t": 115.89910999999995, "r": 480.59735, "b": 128.58112000000006, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Recognition", "bbox": {"l": 266.67499, "t": 133.83209, "r": 348.68506, "b": 146.51409999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "key_value_region", "id": 16, "page_no": 0, 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"label": "text", "bbox": {"l": 163.1111, "t": 270.30115, "r": 452.24878000000007, "b": 464.73447, "coord_origin": "TOPLEFT"}, "confidence": 0.9807655215263367, "cells": [{"id": 40, "text": "Abstract.", "bbox": {"l": 163.1111, "t": 270.30115, "r": 206.6358, "b": 278.22748, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Extracting tables from documents is a crucial task in any", "bbox": {"l": 211.6171, "t": 270.36395000000005, "r": 452.2447199999999, "b": 278.43364999999994, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "document conversion pipeline. Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.93040531873703, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. 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Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.93040531873703, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of"}], "headers": [{"label": "page_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8899644017219543, "cells": [{"id": 74, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "C", "bbox": {"l": 396.41107, "t": 280.98352, "r": 402.97336, "b": 289.50903, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", 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On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580380082130432, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.907667338848114, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938881158828735, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "picture", "bbox": {"l": 148.45362854003906, "t": 208.37408447265625, "r": 464.3610534667969, "b": 425.8468322753906, 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(SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580380082130432, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.907667338848114, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938881158828735, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45362854003906, "t": 208.37408447265625, "r": 464.3610534667969, "b": 425.8468322753906, "coord_origin": "TOPLEFT"}, "confidence": 0.9688884615898132, "cells": [], "children": [{"id": 46, "label": "text", "bbox": {"l": 154.3298, "t": 213.57457999999997, "r": 159.79837, "b": 220.67920000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 52, "text": "A", "bbox": {"l": 154.3298, "t": 213.57457999999997, "r": 159.79837, "b": 220.67920000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 47, "label": "text", "bbox": {"l": 321.07053, "t": 213.57457999999997, "r": 326.53909, 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On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "body": [{"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938881158828735, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45362854003906, "t": 208.37408447265625, "r": 464.3610534667969, "b": 425.8468322753906, "coord_origin": "TOPLEFT"}, "confidence": 0.9688884615898132, "cells": [], "children": [{"id": 46, "label": "text", "bbox": {"l": 154.3298, "t": 213.57457999999997, "r": 159.79837, "b": 220.67920000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 52, "text": "A", "bbox": {"l": 154.3298, "t": 213.57457999999997, "r": 159.79837, "b": 220.67920000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 47, "label": "text", "bbox": {"l": 321.07053, "t": 213.57457999999997, "r": 326.53909, 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"coord_origin": "TOPLEFT"}, "confidence": 0.9505027532577515, "cells": [{"id": 118, "text": "today,", "bbox": {"l": 134.765, "t": 452.31378, "r": 161.32928, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "table detection", "bbox": {"l": 164.269, "t": 452.31378, "r": 226.28617999999997, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "in documents is a well understood problem, and the latest", "bbox": {"l": 229.992, "t": 452.31378, "r": 480.59232000000003, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "state-of-the-art (SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "headers": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580380082130432, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.907667338848114, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325800538063049, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439422488212585, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325800538063049, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439422488212585, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "body": [{"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439422488212585, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "headers": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325800538063049, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.78900545835495, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443227767944, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290871620178223, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393904209136963, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.78900545835495, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443227767944, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290871620178223, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393904209136963, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "body": [{"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443227767944, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290871620178223, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393904209136963, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "headers": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.78900545835495, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.926919162273407, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696111440658569, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "picture", "bbox": {"l": 137.41452026367188, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387974739074707, "cells": [], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844798445701599, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.926919162273407, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696111440658569, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41452026367188, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387974739074707, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844798445701599, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}], "body": [{"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41452026367188, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387974739074707, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844798445701599, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}], "headers": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.926919162273407, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696111440658569, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919059038162231, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633575081825256, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860696792603, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671324491500854, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556634783744812, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184003472328186, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438936114311218, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636077642440796, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443691968917847, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919059038162231, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633575081825256, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860696792603, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671324491500854, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556634783744812, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184003472328186, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438936114311218, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636077642440796, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443691968917847, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "body": [{"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633575081825256, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860696792603, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671324491500854, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556634783744812, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184003472328186, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438936114311218, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636077642440796, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443691968917847, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "headers": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919059038162231, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 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"coord_origin": "TOPLEFT"}}, {"id": 33, "text": "L", "bbox": {"l": 411.86395, "t": 168.06195000000002, "r": 417.38943, "b": 177.38373, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "L", "bbox": {"l": 423.33563, "t": 167.93439, "r": 428.86111, "b": 177.25616000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "X", "bbox": {"l": 387.13593, "t": 180.78576999999996, "r": 393.76453, "b": 190.10753999999997, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "C", "bbox": {"l": 282.2594, "t": 244.50878999999998, "r": 289.43457, "b": 253.83056999999997, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "U", "bbox": {"l": 282.11035, "t": 256.85022000000004, "r": 289.28552, "b": 266.172, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "U", "bbox": {"l": 282.40848, "t": 269.13300000000004, "r": 289.58365, "b": 278.45477000000005, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "L", "bbox": {"l": 295.52902, "t": 244.49347, "r": 301.0545, "b": 253.81525, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "L", "bbox": {"l": 307.46613, "t": 244.57372999999995, "r": 312.99161, "b": 253.89550999999994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "L", "bbox": {"l": 318.76886, "t": 244.44037000000003, "r": 324.29434, "b": 253.76215000000002, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "X", "bbox": {"l": 294.9021, "t": 256.70154, "r": 301.03976, "b": 266.02332, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "X X", "bbox": {"l": 307.17743, "t": 256.70154, "r": 325.59039, "b": 266.02332, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "X", "bbox": {"l": 294.78949, "t": 269.25420999999994, "r": 300.92715, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "X X", "bbox": {"l": 307.06482, "t": 269.25420999999994, "r": 325.47778, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "C", "bbox": {"l": 195.93939, "t": 268.74798999999996, "r": 203.11456, "b": 278.06976, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "L", "bbox": {"l": 209.20891, "t": 268.73267, "r": 214.73439, "b": 278.05444, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "L", "bbox": {"l": 221.14551, "t": 268.81293000000005, "r": 226.67099, "b": 278.13469999999995, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "L", "bbox": {"l": 232.44858, "t": 268.67957, "r": 237.97405999999998, "b": 278.00134, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "C", "bbox": {"l": 196.21715, "t": 244.53961000000004, "r": 203.39232, "b": 253.86139000000003, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "C", "bbox": {"l": 250.32143, "t": 244.09813999999994, "r": 257.49661, "b": 253.41992000000005, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "U", "bbox": {"l": 250.17235999999997, "t": 256.43951000000004, "r": 257.34753, "b": 265.76129000000003, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "U", "bbox": {"l": 250.47049000000004, "t": 268.72222999999997, "r": 257.64566, "b": 278.04400999999996, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "1", "bbox": {"l": 334.51135, "t": 242.99463000000003, "r": 337.22485, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "- simple cells: \"C\"", "bbox": {"l": 339.93835, "t": 242.99463000000003, "r": 391.49472, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 334.51135, "t": 252.93255999999997, "r": 337.33313, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "- horizontal merges: \"C\", \"L\"", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "3", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "- vertical merges: \"C\", \"U\"", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "4", "bbox": {"l": 334.51135, "t": 272.80841, "r": 337.30188, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "- 2d merges: \"C\", \"L\", \"U\", \"X\"", "bbox": {"l": 340.09241, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "1", "bbox": {"l": 185.67178, "t": 244.04224, "r": 189.35544, "b": 250.25671, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "2", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "3", "bbox": {"l": 239.34152, "t": 243.62523999999996, "r": 243.02518, "b": 249.83972000000006, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "4", "bbox": {"l": 271.32852, "t": 243.49390000000005, "r": 275.01218, "b": 249.70836999999995, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "2", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "1", "bbox": {"l": 257.24402, "t": 189.961, "r": 260.92767, "b": 196.17548, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 186.87526, "t": 177.97668, "r": 190.55891, "b": 184.19115999999997, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "4", "bbox": {"l": 196.48746, "t": 169.01520000000005, "r": 200.17111, "b": 175.22968000000003, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "A", "bbox": {"l": 169.74728, "t": 167.88225999999997, "r": 175.72659, "b": 175.65039000000002, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "B", "bbox": {"l": 169.74728, "t": 206.83867999999995, "r": 175.72659, "b": 214.60681, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "C", "bbox": {"l": 274.29419, "t": 168.27972, "r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "D", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "E", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326505661010742, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749722838401794, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "picture", "bbox": {"l": 164.6502227783203, "t": 163.79708862304688, "r": 449.55072021484375, "b": 280.3410339355469, "coord_origin": "TOPLEFT"}, "confidence": 0.7868288159370422, "cells": [], "children": [{"id": 77, "label": "text", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 66, "text": "2", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 435.16009999999994, "t": 167.69011999999998, "r": 447.86273, "b": 177.01189999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 24, "text": "NL", "bbox": {"l": 435.16009999999994, "t": 167.69011999999998, "r": 447.86273, "b": 177.01189999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 81, "label": "text", "bbox": {"l": 169.74728, "t": 167.88225999999997, "r": 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rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506187319755554, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247231602668762, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259926080703735, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420595765113831, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, 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First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590607285499573, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326505661010742, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749722838401794, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}, {"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 3. 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"text": "C", "bbox": {"l": 195.93939, "t": 268.74798999999996, "r": 203.11456, "b": 278.06976, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 61, "label": "text", "bbox": {"l": 221.14551, "t": 268.81293000000005, "r": 226.67099, "b": 278.13469999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 48, "text": "L", "bbox": {"l": 221.14551, "t": 268.81293000000005, "r": 226.67099, "b": 278.13469999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 51, "label": "text", "bbox": {"l": 282.40848, "t": 269.13300000000004, "r": 289.58365, "b": 278.45477000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 38, "text": "U", "bbox": {"l": 282.40848, "t": 269.13300000000004, "r": 289.58365, "b": 278.45477000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 57, "label": "text", "bbox": {"l": 294.78949, "t": 269.25420999999994, "r": 300.92715, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 44, "text": "X", "bbox": {"l": 294.78949, "t": 269.25420999999994, "r": 300.92715, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 58, "label": "text", "bbox": {"l": 307.06482, "t": 269.25420999999994, "r": 325.47778, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 45, "text": "X X", "bbox": {"l": 307.06482, "t": 269.25420999999994, "r": 325.47778, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 334.51135, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.5250746011734009, "cells": [{"id": 60, "text": "4", "bbox": {"l": 334.51135, "t": 272.80841, "r": 337.30188, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "- 2d merges: \"C\", \"L\", \"U\", \"X\"", "bbox": {"l": 340.09241, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 134.765, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}, "confidence": 0.9233025312423706, "cells": [{"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.2 Language Syntax"}, {"label": "text", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "text", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}, "confidence": 0.9169168472290039, "cells": [{"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL representation follows these syntax rules:"}, {"label": "list_item", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 138.97299, "t": 347.17081, "r": 480.58902, "b": 367.93375, "coord_origin": "TOPLEFT"}, "confidence": 0.958811342716217, "cells": [{"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506187319755554, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247231602668762, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259926080703735, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420595765113831, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785566329956055, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590607285499573, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "body": [{"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding"}, {"label": "picture", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "picture", "bbox": {"l": 164.6502227783203, "t": 163.79708862304688, "r": 449.55072021484375, "b": 280.3410339355469, "coord_origin": "TOPLEFT"}, "confidence": 0.7868288159370422, "cells": [], "children": [{"id": 77, "label": "text", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 66, "text": "2", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 435.16009999999994, "t": 167.69011999999998, "r": 447.86273, "b": 177.01189999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, 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"text": "C", "bbox": {"l": 195.93939, "t": 268.74798999999996, "r": 203.11456, "b": 278.06976, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 61, "label": "text", "bbox": {"l": 221.14551, "t": 268.81293000000005, "r": 226.67099, "b": 278.13469999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 48, "text": "L", "bbox": {"l": 221.14551, "t": 268.81293000000005, "r": 226.67099, "b": 278.13469999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 51, "label": "text", "bbox": {"l": 282.40848, "t": 269.13300000000004, "r": 289.58365, "b": 278.45477000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 38, "text": "U", "bbox": {"l": 282.40848, "t": 269.13300000000004, "r": 289.58365, "b": 278.45477000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 57, "label": "text", "bbox": {"l": 294.78949, "t": 269.25420999999994, "r": 300.92715, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 44, "text": "X", "bbox": {"l": 294.78949, "t": 269.25420999999994, "r": 300.92715, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 58, "label": "text", "bbox": {"l": 307.06482, "t": 269.25420999999994, "r": 325.47778, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 45, "text": "X X", "bbox": {"l": 307.06482, "t": 269.25420999999994, "r": 325.47778, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 334.51135, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.5250746011734009, "cells": [{"id": 60, "text": "4", "bbox": {"l": 334.51135, "t": 272.80841, "r": 337.30188, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "- 2d merges: \"C\", \"L\", \"U\", \"X\"", "bbox": {"l": 340.09241, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 134.765, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}, "confidence": 0.9233025312423706, "cells": [{"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.2 Language Syntax"}, {"label": "text", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "text", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}, "confidence": 0.9169168472290039, "cells": [{"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL representation follows these syntax rules:"}, {"label": "list_item", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 138.97299, "t": 347.17081, "r": 480.58902, "b": 367.93375, "coord_origin": "TOPLEFT"}, "confidence": 0.958811342716217, "cells": [{"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506187319755554, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247231602668762, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259926080703735, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420595765113831, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785566329956055, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590607285499573, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "headers": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326505661010742, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749722838401794, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.", "bbox": {"l": 147.30025, "t": 540.73164, "r": 149.70605, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Item", "bbox": {"l": 150.90895, "t": 540.73164, "r": 155.72055, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Amount", "bbox": {"l": 162.75987, "t": 535.3938, "r": 172.2963, "b": 537.76224, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Names", "bbox": {"l": 147.63603, "t": 535.3661500000001, "r": 155.91753, "b": 537.73459, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1000", "bbox": {"l": 158.48466, "t": 540.73164, "r": 164.10178, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "500", "bbox": {"l": 158.48466, "t": 544.67065, "r": 162.69737, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "3500", "bbox": {"l": 158.48466, "t": 548.91264, "r": 164.10178, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "150", "bbox": {"l": 158.48466, "t": 553.15465, "r": 162.69737, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "unit", "bbox": {"l": 168.81696, "t": 540.73164, "r": 172.88876, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "unit", "bbox": {"l": 168.81696, "t": 544.67065, "r": 172.88876, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "unit", "bbox": {"l": 168.81696, "t": 548.91264, "r": 172.88876, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "unit", "bbox": {"l": 168.81696, "t": 553.15465, "r": 172.88876, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "2.", "bbox": {"l": 147.30025, "t": 544.67065, "r": 149.70605, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Item", "bbox": {"l": 150.90895, "t": 544.67065, "r": 155.72055, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "3.", "bbox": {"l": 147.30025, "t": 548.91264, "r": 149.70605, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Item", "bbox": {"l": 150.90895, "t": 548.91264, "r": 155.72055, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "4.", "bbox": {"l": 147.30025, "t": 553.15465, "r": 149.70605, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Item", "bbox": {"l": 150.90895, "t": 553.15465, "r": 155.72055, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Extracted", "bbox": {"l": 152.05046, "t": 517.0098, "r": 171.24945, "b": 521.27298, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Table Images", "bbox": {"l": 148.13347, "t": 522.3122900000001, "r": 175.16759, "b": 526.57547, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Standardized", "bbox": {"l": 193.53331, "t": 524.51422, "r": 220.31973, "b": 528.7774, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Images", "bbox": {"l": 199.47311, "t": 529.8167100000001, "r": 214.37889, "b": 534.0799, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "BBox", "bbox": {"l": 273.61066, "t": 509.9053, "r": 284.47275, "b": 514.16849, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Decoder", "bbox": {"l": 270.45187, "t": 513.6928399999999, "r": 287.63242, "b": 517.9560200000001, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "BBoxes", "bbox": {"l": 332.47852, "t": 508.14438, "r": 348.14014, "b": 512.40756, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "BBoxes can be", "bbox": {"l": 376.68622, "t": 521.12024, "r": 407.25497, "b": 525.38342, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "traced back to the", "bbox": {"l": 373.90869, "t": 525.66525, "r": 410.03506, "b": 529.92844, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "original image to", "bbox": {"l": 375.29871, "t": 530.21024, "r": 408.64902, "b": 534.47342, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "extract content", "bbox": {"l": 377.06747, "t": 534.75522, "r": 406.88312, "b": 539.01843, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Structure Tags sequence", "bbox": {"l": 383.56683, "t": 563.24176, "r": 433.76544, "b": 567.50497, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "provide full description of", "bbox": {"l": 383.52768, "t": 567.78676, "r": 433.80764999999997, "b": 572.04997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "the table structure", "bbox": {"l": 390.47522, "t": 572.33177, "r": 426.85703, "b": 576.59499, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Structure Tags", "bbox": {"l": 293.94702, "t": 577.89143, "r": 323.1691, "b": 582.15465, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "in OTSL format", "bbox": {"l": 293.94702, "t": 582.43648, "r": 324.59396, "b": 586.69969, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "BBoxes in sync", "bbox": {"l": 333.07819, "t": 541.82269, "r": 364.14691, "b": 546.08591, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "with tag sequence", "bbox": {"l": 333.07819, "t": 545.6102, "r": 369.71542, "b": 549.87341, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Encoder", "bbox": {"l": 232.65881000000002, "t": 515.24139, "r": 249.58894000000004, "b": 519.50458, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "Structure", "bbox": {"l": 269.8219, "t": 545.97102, "r": 288.26279, "b": 550.23424, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Decoder", "bbox": {"l": 270.45187, "t": 549.75851, "r": 287.63242, "b": 554.0217299999999, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "[x1, y2, x2, y2]", "bbox": {"l": 332.17676, "t": 515.91205, "r": 358.11206, "b": 520.17523, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "[x1', y2', x2', y2']", "bbox": {"l": 332.17676, "t": 521.9720500000001, "r": 361.58298, "b": 526.23523, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "[x1'', y2'', x2'', y2'']", "bbox": {"l": 332.17676, "t": 528.03204, "r": 364.76474, "b": 532.29523, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "...", "bbox": {"l": 332.17676, "t": 534.09204, "r": 335.96548, "b": 538.35524, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "1", "bbox": {"l": 326.8894, "t": 516.39508, "r": 329.41641, "b": 520.6582599999999, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "2", "bbox": {"l": 327.04089, "t": 522.4247700000001, "r": 329.5679, "b": 526.68796, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3", "bbox": {"l": 327.04089, "t": 528.51508, "r": 329.5679, "b": 532.77826, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "3", "bbox": {"l": 424.14102, "t": 527.4428399999999, "r": 426.66803, "b": 531.7060200000001, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "2", "bbox": {"l": 453.0018, "t": 517.4539500000001, "r": 455.52881, "b": 521.71713, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "1", "bbox": {"l": 423.85825, "t": 517.06281, "r": 426.38525, "b": 521.32599, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "C", "bbox": {"l": 333.4342, "t": 557.36679, "r": 337.27542, "b": 562.35719, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "C", "bbox": {"l": 340.35397, "t": 557.31679, "r": 344.19519, "b": 562.30719, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "C", "bbox": {"l": 340.30978, "t": 563.8653899999999, "r": 344.151, "b": 568.8557900000001, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "C", "bbox": {"l": 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"text": "C", "bbox": {"l": 340.27948, "t": 583.39737, "r": 344.1207, "b": 588.38777, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "C", "bbox": {"l": 346.76874, "t": 583.40068, "r": 350.60995, "b": 588.39108, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "C", "bbox": {"l": 333.56549, "t": 583.35474, "r": 337.40671, "b": 588.34514, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "NL", "bbox": {"l": 353.03326, "t": 556.8831299999999, "r": 359.83362, "b": 561.87354, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "NL", "bbox": {"l": 353.18604, "t": 563.58044, "r": 359.98639, "b": 568.57085, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "NL", "bbox": {"l": 353.19864, "t": 570.1623500000001, "r": 359.99899, "b": 575.15276, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "NL", "bbox": {"l": 353.1532, "t": 576.76611, "r": 359.95355, "b": 581.75652, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "NL", "bbox": {"l": 353.26935, "t": 583.40628, "r": 360.0697, "b": 588.3966800000001, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "L", "bbox": {"l": 347.37979, "t": 557.08235, "r": 350.33786, "b": 562.07275, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3", "bbox": {"l": 331.14026, "t": 564.2907700000001, "r": 333.66727, "b": 568.55399, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "2", "bbox": {"l": 340.80972, "t": 554.59312, "r": 343.33673, "b": 558.85634, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "1", "bbox": {"l": 330.97992, "t": 554.83035, "r": 333.50693, "b": 559.09357, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.58792, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.58871, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301324844360352, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8631827235221863, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464746713638306, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515273571014404, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. 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664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9512704014778137, "cells": [{"id": 105, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.58792, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.58871, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301324844360352, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8"}, {"label": "page_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8631827235221863, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464746713638306, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515273571014404, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297955632209778, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 4. 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null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5917400000001, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9512704014778137, "cells": [{"id": 105, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.58792, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.58871, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}], "body": [{"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464746713638306, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515273571014404, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297955632209778, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 4. 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The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}], "headers": [{"label": "page_header", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301324844360352, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8"}, {"label": "page_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8631827235221863, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. 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508.15179, "r": 149.40205, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378157615662, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8857628107070923, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59579, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579682350158691, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.957078218460083, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 144.592, "t": 341.73495, "r": 470.76056, "b": 467.9617, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 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Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579682350158691, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. 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However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.957078218460083, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 144.592, "t": 341.73495, "r": 470.76056, "b": 467.9617, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 231.43106, "b": 362.75570999999997, 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{"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.5957599999999, "b": 617.03474, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5957599999999, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59579, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579682350158691, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.957078218460083, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 144.592, "t": 341.73495, "r": 470.76056, "b": 467.9617, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 231.43106, "b": 362.75570999999997, 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{"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.5957599999999, "b": 617.03474, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5957599999999, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378157615662, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8857628107070923, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9"}]}}, {"page_no": 9, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "10", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 2.", "bbox": {"l": 134.765, "t": 115.83618000000001, "r": 173.09366, "b": 123.76251000000002, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "TSR and cell detection results compared between OTSL and HTML on", "bbox": {"l": 181.30299, "t": 115.89899000000003, "r": 480.59151999999995, "b": 123.96868999999992, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using Table-", "bbox": {"l": 134.765, "t": 126.85797000000014, "r": 480.59357000000006, "b": 134.92767000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Former [9] (with enc=6, dec=6, heads=8).", "bbox": {"l": 134.765, "t": 137.81696, "r": 305.95691, "b": 145.88666, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Data set", "bbox": {"l": 160.782, "t": 166.55895999999996, "r": 194.99779, "b": 174.62865999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Language", "bbox": {"l": 215.52499000000003, "t": 166.534, "r": 254.04465, "b": 174.6037, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "TEDs", "bbox": {"l": 300.397, "t": 161.07898, "r": 323.99118, "b": 169.14868, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "mAP(0.75)", "bbox": {"l": 370.345, "t": 166.55895999999996, "r": 414.74661, "b": 174.62865999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Inference", "bbox": {"l": 426.737, "t": 161.07898, "r": 463.10830999999996, "b": 169.14868, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "time (secs)", "bbox": {"l": 423.11401, "t": 172.03796, "r": 466.72656, "b": 180.10766999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "simple", "bbox": {"l": 262.41299, "t": 174.03101000000004, "r": 288.0596, "b": 182.10071000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "complex", "bbox": {"l": 296.42899, "t": 174.03101000000004, "r": 329.44687, "b": 182.10071000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "all", "bbox": {"l": 345.03299, "t": 174.03101000000004, "r": 354.75793, "b": 182.10071000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "PubTabNet", "bbox": {"l": 154.53799, "t": 192.85999000000004, "r": 201.24129, "b": 200.92969000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "OTSL", "bbox": {"l": 222.43700000000004, "t": 187.38098000000002, "r": 247.13226000000003, "b": 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In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap 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{"id": 56, "text": "Qualitative Results", "bbox": {"l": 160.85904, "t": 288.91479, "r": 257.08679, "b": 297.72173999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.3 Qualitative Results"}, {"label": "text", "id": 1, "page_no": 9, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 309.86078, "r": 480.58981, "b": 366.47769, "coord_origin": "TOPLEFT"}, "confidence": 0.9834067225456238, "cells": [{"id": 57, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5", "bbox": {"l": 134.765, "t": 309.86078, "r": 480.58777, "b": 318.65775, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "demonstrates less overlap and more accurate bounding boxes with OTSL. In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. 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The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). 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The OTSL model (B) captured repeating pattern of horizontally", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.58823, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 480.5881999999999, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and", "bbox": {"l": 134.765, "t": 158.73895000000005, "r": 480.58838000000003, "b": 166.80864999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 169.69794000000002, "r": 415.84454, "b": 177.76764000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"label": "picture", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 168.39263916015625, "t": 181.96795654296875, "r": 447.35272216796875, "b": 634.003173828125, "coord_origin": "TOPLEFT"}, "confidence": 0.7615750432014465, "cells": [], "children": [{"id": 10, "label": "text", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 15, "text": "A", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 181.89114, "t": 288.35962000000006, "r": 239.23492, "b": 294.2947700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 13, "text": "Repeating pattern of", "bbox": {"l": 181.89114, "t": 288.35962000000006, "r": 239.23492, "b": 294.2947700000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 181.89114, "t": 294.89423, "r": 251.52917, "b": 300.82938, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 14, "text": "horizontally merged cells", "bbox": {"l": 181.89114, "t": 294.89423, "r": 251.52917, "b": 300.82938, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 8, "text": "B", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 172.27777, "t": 381.36288, "r": 180.18666, "b": 403.40067, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 17, "text": "OTSL", "bbox": {"l": 172.27777, "t": 381.36288, "r": 180.18666, "b": 403.40067, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 293.64209, "t": 465.59784, "r": 437.50800000000004, "b": 471.53299, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 12, "text": "Repeating pattern is well represented in predictions", "bbox": {"l": 293.64209, "t": 465.59784, "r": 437.50800000000004, "b": 471.53299, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 9, "text": "C", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 172.27747, "t": 555.7769499999999, "r": 180.18663, "b": 578.7478, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 18, "text": "HTML", "bbox": {"l": 172.27747, "t": 555.7769499999999, "r": 180.18663, "b": 578.7478, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 292.18976, "t": 607.80609, "r": 381.54663, "b": 613.7412400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 16, "text": "Bounding box drifting at the end", "bbox": {"l": 292.18976, "t": 607.80609, "r": 381.54663, "b": 613.7412400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "Horizontally merged cells are not present", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 10, "text": "Incorrect end of HTML sequence", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9301635026931763, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "page_header", "bbox": {"l": 471.37561, "t": 93.77099999999996, "r": 480.5894799999999, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9007187485694885, "cells": [{"id": 1, "text": "11", "bbox": {"l": 471.37561, "t": 93.77099999999996, "r": 480.5894799999999, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. 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IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8610868453979492, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8927640914916992, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156443476676941, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806177020072937, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868756532669067, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403368830680847, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698705077171326, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 139.37097, "t": 567.51884, "r": 480.5920100000001, "b": 608.46561, "coord_origin": "TOPLEFT"}, "confidence": 0.973068118095398, "cells": [{"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 480.58731000000006, "b": 631.18761, "coord_origin": "TOPLEFT"}, "confidence": 0.9617277979850769, "cells": [{"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 139.37097, "t": 634.88084, "r": 480.58826, "b": 664.86761, "coord_origin": "TOPLEFT"}, "confidence": 0.975471019744873, "cells": [{"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8610868453979492, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}, {"label": "page_header", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8927640914916992, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156443476676941, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806177020072937, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868756532669067, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403368830680847, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698705077171326, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785"}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 139.37097, "t": 567.51884, "r": 480.5920100000001, "b": 608.46561, "coord_origin": "TOPLEFT"}, "confidence": 0.973068118095398, "cells": [{"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545561. Springer International Publishing, Cham (2022)"}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 480.58731000000006, "b": 631.18761, "coord_origin": "TOPLEFT"}, "confidence": 0.9617277979850769, "cells": [{"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv preprint arXiv:1908.04729 (2019)"}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 139.37097, "t": 634.88084, "r": 480.58826, "b": 664.86761, "coord_origin": "TOPLEFT"}, "confidence": 0.975471019744873, "cells": [{"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)"}], "body": [{"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156443476676941, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806177020072937, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868756532669067, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403368830680847, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698705077171326, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785"}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 139.37097, "t": 567.51884, "r": 480.5920100000001, "b": 608.46561, "coord_origin": "TOPLEFT"}, "confidence": 0.973068118095398, "cells": [{"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545561. 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Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv preprint arXiv:1908.04729 (2019)"}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 139.37097, "t": 634.88084, "r": 480.58826, "b": 664.86761, "coord_origin": "TOPLEFT"}, "confidence": 0.975471019744873, "cells": [{"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. 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Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 115.89910999999995, "r": 480.59735, "b": 146.51409999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.89183509349823, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure", "bbox": {"l": 134.765, "t": 115.89910999999995, "r": 480.59735, "b": 128.58112000000006, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Recognition", "bbox": {"l": 266.67499, "t": 133.83209, "r": 348.68506, "b": 146.51409999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "key_value_region", "bbox": {"l": 137.36988830566406, "t": 168.1707305908203, "r": 476.8817443847656, 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This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.93040531873703, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. 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"id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 163.1111, "t": 270.30115, "r": 452.24878000000007, "b": 464.73447, "coord_origin": "TOPLEFT"}, "confidence": 0.9807655215263367, "cells": [{"id": 40, "text": "Abstract.", "bbox": {"l": 163.1111, "t": 270.30115, "r": 206.6358, "b": 278.22748, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Extracting tables from documents is a crucial task in any", "bbox": {"l": 211.6171, "t": 270.36395000000005, "r": 452.2447199999999, "b": 278.43364999999994, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "document conversion pipeline. Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.93040531873703, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. 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Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.93040531873703, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of"}], "headers": [{"label": "page_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8899644017219543, "cells": [{"id": 74, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "C", "bbox": {"l": 396.41107, "t": 280.98352, "r": 402.97336, "b": 289.50903, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", 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"vocabulary:", "bbox": {"l": 393.75256, "t": 399.7947700000001, "r": 432.48929, "b": 406.89935, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "5", "bbox": {"l": 434.5896000000001, "t": 399.7947700000001, "r": 438.80083999999994, "b": 406.89935, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "tokens", "bbox": {"l": 440.90573, "t": 399.7947700000001, "r": 463.22235, "b": 406.89935, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "D OTSL", "bbox": {"l": 384.11816, "t": 258.54718, "r": 413.99307, "b": 265.65179, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "sequence length:", "bbox": {"l": 393.75256, "t": 266.67505000000006, "r": 451.45129000000003, "b": 273.77966000000004, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "30", "bbox": {"l": 453.55083999999994, "t": 266.67505000000006, "r": 461.97485, "b": 273.77966000000004, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vocabulary for this table:", "bbox": {"l": 151.79318, "t": 399.76016, "r": 233.89371000000003, "b": 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154.50595, "t": 258.60095, "r": 159.62473, "b": 265.70556999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "HTML", "bbox": {"l": 164.74348, "t": 258.60095, "r": 185.21857, "b": 265.70556999999997, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "sequence length:", "bbox": {"l": 164.3548, "t": 266.49707, "r": 222.05352999999997, "b": 273.60168, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "55", "bbox": {"l": 224.15326, "t": 266.49707, "r": 232.57729, "b": 273.60168, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "today,", "bbox": {"l": 134.765, "t": 452.31378, "r": 161.32928, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "table detection", "bbox": {"l": 164.269, "t": 452.31378, "r": 226.28617999999997, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "in documents is a well understood problem, and the latest", "bbox": {"l": 229.992, "t": 452.31378, "r": 480.59232000000003, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "state-of-the-art (SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580380082130432, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.907667338848114, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938881158828735, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "picture", "bbox": {"l": 148.45362854003906, "t": 208.37408447265625, "r": 464.3610534667969, "b": 425.8468322753906, 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On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580380082130432, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.907667338848114, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938881158828735, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45362854003906, "t": 208.37408447265625, "r": 464.3610534667969, "b": 425.8468322753906, "coord_origin": "TOPLEFT"}, "confidence": 0.9688884615898132, "cells": [], "children": [{"id": 46, "label": "text", "bbox": {"l": 154.3298, "t": 213.57457999999997, "r": 159.79837, "b": 220.67920000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 52, "text": "A", "bbox": {"l": 154.3298, "t": 213.57457999999997, "r": 159.79837, "b": 220.67920000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 47, "label": "text", "bbox": {"l": 321.07053, "t": 213.57457999999997, "r": 326.53909, 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On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "body": [{"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938881158828735, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45362854003906, "t": 208.37408447265625, "r": 464.3610534667969, "b": 425.8468322753906, "coord_origin": "TOPLEFT"}, "confidence": 0.9688884615898132, "cells": [], "children": [{"id": 46, "label": "text", "bbox": {"l": 154.3298, "t": 213.57457999999997, "r": 159.79837, "b": 220.67920000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 52, "text": "A", "bbox": {"l": 154.3298, "t": 213.57457999999997, "r": 159.79837, "b": 220.67920000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 47, "label": "text", "bbox": {"l": 321.07053, "t": 213.57457999999997, "r": 326.53909, 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On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "headers": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580380082130432, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.907667338848114, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325800538063049, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439422488212585, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325800538063049, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439422488212585, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "body": [{"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439422488212585, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "headers": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325800538063049, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.78900545835495, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443227767944, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290871620178223, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393904209136963, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.78900545835495, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443227767944, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290871620178223, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393904209136963, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "body": [{"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443227767944, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290871620178223, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393904209136963, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "headers": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.78900545835495, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.926919162273407, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696111440658569, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "picture", "bbox": {"l": 137.41452026367188, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387974739074707, "cells": [], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844798445701599, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.926919162273407, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696111440658569, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41452026367188, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387974739074707, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844798445701599, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}], "body": [{"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41452026367188, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387974739074707, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844798445701599, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}], "headers": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.926919162273407, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696111440658569, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919059038162231, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633575081825256, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860696792603, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671324491500854, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556634783744812, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184003472328186, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438936114311218, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636077642440796, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443691968917847, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919059038162231, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633575081825256, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860696792603, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671324491500854, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556634783744812, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184003472328186, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438936114311218, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636077642440796, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443691968917847, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "body": [{"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633575081825256, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860696792603, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671324491500854, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556634783744812, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184003472328186, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438936114311218, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636077642440796, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443691968917847, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "headers": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919059038162231, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 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"text": "L", "bbox": {"l": 307.46613, "t": 244.57372999999995, "r": 312.99161, "b": 253.89550999999994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "L", "bbox": {"l": 318.76886, "t": 244.44037000000003, "r": 324.29434, "b": 253.76215000000002, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "X", "bbox": {"l": 294.9021, "t": 256.70154, "r": 301.03976, "b": 266.02332, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "X X", "bbox": {"l": 307.17743, "t": 256.70154, "r": 325.59039, "b": 266.02332, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "X", "bbox": {"l": 294.78949, "t": 269.25420999999994, "r": 300.92715, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "X X", "bbox": {"l": 307.06482, "t": 269.25420999999994, "r": 325.47778, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "C", "bbox": {"l": 195.93939, "t": 268.74798999999996, "r": 203.11456, "b": 278.06976, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "L", "bbox": {"l": 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334.51135, "t": 242.99463000000003, "r": 337.22485, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "- simple cells: \"C\"", "bbox": {"l": 339.93835, "t": 242.99463000000003, "r": 391.49472, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 334.51135, "t": 252.93255999999997, "r": 337.33313, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "- horizontal merges: \"C\", \"L\"", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "3", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "- vertical merges: \"C\", \"U\"", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "4", "bbox": {"l": 334.51135, "t": 272.80841, "r": 337.30188, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "- 2d merges: \"C\", \"L\", \"U\", \"X\"", "bbox": {"l": 340.09241, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "1", "bbox": {"l": 185.67178, "t": 244.04224, "r": 189.35544, "b": 250.25671, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "2", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "3", "bbox": {"l": 239.34152, "t": 243.62523999999996, "r": 243.02518, "b": 249.83972000000006, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "4", "bbox": {"l": 271.32852, "t": 243.49390000000005, "r": 275.01218, "b": 249.70836999999995, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "2", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "1", "bbox": {"l": 257.24402, "t": 189.961, "r": 260.92767, "b": 196.17548, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 186.87526, "t": 177.97668, "r": 190.55891, "b": 184.19115999999997, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "4", "bbox": {"l": 196.48746, "t": 169.01520000000005, "r": 200.17111, "b": 175.22968000000003, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "A", "bbox": {"l": 169.74728, "t": 167.88225999999997, "r": 175.72659, "b": 175.65039000000002, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "B", "bbox": {"l": 169.74728, "t": 206.83867999999995, "r": 175.72659, "b": 214.60681, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "C", "bbox": {"l": 274.29419, "t": 168.27972, "r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "D", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "E", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326505661010742, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749722838401794, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, 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463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, 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First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590607285499573, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326505661010742, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749722838401794, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}, {"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 3. 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Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506187319755554, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247231602668762, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259926080703735, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420595765113831, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785566329956055, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590607285499573, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "body": [{"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding"}, {"label": "picture", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "picture", "bbox": {"l": 164.6502227783203, "t": 163.79708862304688, "r": 449.55072021484375, "b": 280.3410339355469, "coord_origin": "TOPLEFT"}, "confidence": 0.7868288159370422, "cells": [], "children": [{"id": 77, "label": "text", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 66, "text": "2", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 435.16009999999994, "t": 167.69011999999998, "r": 447.86273, "b": 177.01189999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, 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Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506187319755554, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247231602668762, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259926080703735, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420595765113831, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785566329956055, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590607285499573, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "headers": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326505661010742, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749722838401794, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.", "bbox": {"l": 147.30025, "t": 540.73164, "r": 149.70605, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Item", "bbox": {"l": 150.90895, "t": 540.73164, "r": 155.72055, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Amount", "bbox": {"l": 162.75987, "t": 535.3938, "r": 172.2963, "b": 537.76224, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Names", "bbox": {"l": 147.63603, "t": 535.3661500000001, "r": 155.91753, "b": 537.73459, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1000", "bbox": {"l": 158.48466, "t": 540.73164, "r": 164.10178, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "500", "bbox": {"l": 158.48466, "t": 544.67065, "r": 162.69737, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "3500", "bbox": {"l": 158.48466, "t": 548.91264, "r": 164.10178, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "150", "bbox": {"l": 158.48466, "t": 553.15465, "r": 162.69737, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "unit", "bbox": {"l": 168.81696, "t": 540.73164, "r": 172.88876, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "unit", "bbox": {"l": 168.81696, "t": 544.67065, "r": 172.88876, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "unit", "bbox": {"l": 168.81696, "t": 548.91264, "r": 172.88876, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "unit", "bbox": {"l": 168.81696, "t": 553.15465, "r": 172.88876, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "2.", "bbox": {"l": 147.30025, "t": 544.67065, "r": 149.70605, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Item", "bbox": {"l": 150.90895, "t": 544.67065, "r": 155.72055, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "3.", "bbox": {"l": 147.30025, "t": 548.91264, "r": 149.70605, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Item", "bbox": {"l": 150.90895, "t": 548.91264, "r": 155.72055, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "4.", "bbox": {"l": 147.30025, "t": 553.15465, "r": 149.70605, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Item", "bbox": {"l": 150.90895, "t": 553.15465, "r": 155.72055, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Extracted", "bbox": {"l": 152.05046, "t": 517.0098, "r": 171.24945, "b": 521.27298, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Table Images", "bbox": {"l": 148.13347, "t": 522.3122900000001, "r": 175.16759, "b": 526.57547, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Standardized", "bbox": {"l": 193.53331, "t": 524.51422, "r": 220.31973, "b": 528.7774, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Images", "bbox": {"l": 199.47311, "t": 529.8167100000001, "r": 214.37889, "b": 534.0799, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "BBox", "bbox": {"l": 273.61066, "t": 509.9053, "r": 284.47275, "b": 514.16849, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Decoder", "bbox": {"l": 270.45187, "t": 513.6928399999999, "r": 287.63242, "b": 517.9560200000001, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "BBoxes", "bbox": {"l": 332.47852, "t": 508.14438, "r": 348.14014, "b": 512.40756, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "BBoxes can be", "bbox": {"l": 376.68622, "t": 521.12024, "r": 407.25497, "b": 525.38342, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "traced back to the", "bbox": {"l": 373.90869, "t": 525.66525, "r": 410.03506, "b": 529.92844, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "original image to", "bbox": {"l": 375.29871, "t": 530.21024, "r": 408.64902, "b": 534.47342, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "extract content", "bbox": {"l": 377.06747, "t": 534.75522, "r": 406.88312, "b": 539.01843, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Structure Tags sequence", "bbox": {"l": 383.56683, "t": 563.24176, "r": 433.76544, "b": 567.50497, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "provide full description of", "bbox": {"l": 383.52768, "t": 567.78676, "r": 433.80764999999997, "b": 572.04997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "the table structure", "bbox": {"l": 390.47522, "t": 572.33177, "r": 426.85703, "b": 576.59499, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Structure Tags", "bbox": {"l": 293.94702, "t": 577.89143, "r": 323.1691, "b": 582.15465, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "in OTSL format", "bbox": {"l": 293.94702, "t": 582.43648, "r": 324.59396, "b": 586.69969, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "BBoxes in sync", "bbox": {"l": 333.07819, "t": 541.82269, "r": 364.14691, "b": 546.08591, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "with tag sequence", "bbox": {"l": 333.07819, "t": 545.6102, "r": 369.71542, "b": 549.87341, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Encoder", "bbox": {"l": 232.65881000000002, "t": 515.24139, "r": 249.58894000000004, "b": 519.50458, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "Structure", "bbox": {"l": 269.8219, "t": 545.97102, "r": 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the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301324844360352, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8631827235221863, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464746713638306, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515273571014404, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297955632209778, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 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664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9512704014778137, "cells": [{"id": 105, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.58792, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.58871, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301324844360352, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8"}, {"label": "page_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8631827235221863, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464746713638306, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515273571014404, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297955632209778, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.7096710205078, "t": 508.06390380859375, "r": 472.73382568359375, "b": 593.67724609375, "coord_origin": "TOPLEFT"}, "confidence": 0.9303393959999084, "cells": [], "children": [{"id": 34, "label": "text", "bbox": {"l": 332.47852, "t": 508.14438, "r": 348.14014, "b": 512.40756, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 57, "text": "BBoxes", "bbox": {"l": 332.47852, "t": 508.14438, "r": 348.14014, "b": 512.40756, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 273.61066, "t": 509.9053, "r": 284.47275, "b": 514.16849, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 55, "text": "BBox", "bbox": {"l": 273.61066, "t": 509.9053, "r": 284.47275, "b": 514.16849, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, 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The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}], "body": [{"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464746713638306, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515273571014404, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297955632209778, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 4. 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The predicted OTSL structures were converted back to HTML format in"}], "headers": [{"label": "page_header", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301324844360352, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8"}, {"label": "page_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8631827235221863, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 231.43106, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Language", "bbox": {"l": 239.79799999999997, "t": 347.21396, "r": 278.31766, "b": 355.28372, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TEDs", "bbox": {"l": 324.67001, "t": 341.73495, "r": 348.26419, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "mAP", "bbox": {"l": 396.271, "t": 341.73495, "r": 417.12683, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "(0.75)", "bbox": {"l": 394.927, "t": 352.69394000000005, "r": 418.47278, "b": 360.7637, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Inference", "bbox": {"l": 430.771, "t": 341.73495, "r": 467.1423, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "time (secs)", "bbox": {"l": 427.14801, "t": 352.69394000000005, "r": 470.76056, "b": 360.7637, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "simple", "bbox": {"l": 286.686, "t": 354.68594, "r": 312.33261, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "complex", "bbox": {"l": 320.702, "t": 354.68594, "r": 353.71988, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "all", "bbox": {"l": 369.306, "t": 354.68594, "r": 379.03094, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "6", "bbox": {"l": 161.90601, "t": 373.51596, "r": 166.51294, "b": 381.58572, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "6", "bbox": {"l": 209.509, "t": 373.51596, "r": 214.11594, "b": 381.58572, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 368.03595, "r": 271.40527, "b": 376.10571, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "0.965", "bbox": {"l": 289.017, "t": 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396.20599, "t": 407.28894, "r": 417.19275, "b": 415.3587, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3.77", "bbox": {"l": 440.767, "t": 407.28894, "r": 457.14682, "b": 415.3587, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "2", "bbox": {"l": 161.90601, "t": 426.11795, "r": 166.51294, "b": 434.1877099999999, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "4", "bbox": {"l": 209.509, "t": 426.11795, "r": 214.11594, "b": 434.1877099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 420.63895, "r": 271.40527, "b": 428.70871, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "0.923", "bbox": {"l": 289.017, "t": 420.63895, "r": 310.00375, "b": 428.70871, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0.897", "bbox": {"l": 326.71701, "t": 420.63895, "r": 347.70377, "b": 428.70871, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "0.915", "bbox": {"l": 363.67599, "t": 420.63895, "r": 384.66275, "b": 428.70871, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "0.859", "bbox": {"l": 394.61801, "t": 420.57617, "r": 418.77887, "b": 428.50247, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "1.91", "bbox": {"l": 439.52701, "t": 420.57617, "r": 458.38425, "b": 428.50247, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 433.58994, "r": 272.93954, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "0.945", "bbox": {"l": 289.017, "t": 433.58994, "r": 310.00375, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "0.901", "bbox": {"l": 326.71701, "t": 433.58994, "r": 347.70377, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "0.931", "bbox": {"l": 362.08801, "t": 433.5271599999999, "r": 386.24887, "b": 441.45346, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "0.834", "bbox": {"l": 396.20599, "t": 433.58994, "r": 417.19275, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "3.81", "bbox": {"l": 440.767, "t": 433.58994, "r": 457.14682, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "4", "bbox": {"l": 161.90601, "t": 452.41995, "r": 166.51294, "b": 460.48972, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "2", "bbox": {"l": 209.509, "t": 452.41995, "r": 214.11594, "b": 460.48972, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 446.9399399999999, "r": 271.40527, "b": 455.0097, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "0.952", "bbox": {"l": 289.017, "t": 446.9399399999999, "r": 310.00375, "b": 455.0097, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "0.92", "bbox": {"l": 329.021, "t": 446.9399399999999, "r": 345.40082, "b": 455.0097, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "0.942", "bbox": {"l": 362.08801, "t": 446.87717, "r": 386.24887, "b": 454.80347, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "0.857", "bbox": {"l": 394.61801, "t": 446.87717, "r": 418.77887, "b": 454.80347, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1.22", "bbox": {"l": 439.52701, "t": 446.87717, "r": 458.38425, "b": 454.80347, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 459.8919399999999, "r": 272.93954, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "0.944", "bbox": {"l": 289.017, "t": 459.8919399999999, "r": 310.00375, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0.903", "bbox": {"l": 326.71701, "t": 459.8919399999999, "r": 347.70377, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "0.931", "bbox": {"l": 363.67599, "t": 459.8919399999999, "r": 384.66275, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0.824", "bbox": {"l": 396.20599, "t": 459.8919399999999, "r": 417.19275, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "2", "bbox": {"l": 446.65302, "t": 459.8919399999999, "r": 451.25995, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 508.15179, "r": 149.40205, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378157615662, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8857628107070923, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59579, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579682350158691, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.957078218460083, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747375488281, "r": 475.00372314453125, "b": 469.4720764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": 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Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579682350158691, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.957078218460083, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747375488281, "r": 475.00372314453125, "b": 469.4720764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 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458.38425, "b": 467.9617, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}, "confidence": 0.9592539668083191, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 508.15179, "r": 149.40205, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.5957599999999, "b": 617.03474, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5957599999999, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59579, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579682350158691, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.957078218460083, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747375488281, "r": 475.00372314453125, "b": 469.4720764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 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617.03474, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5957599999999, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378157615662, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8857628107070923, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9"}]}}, {"page_no": 9, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "10", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 2.", "bbox": {"l": 134.765, "t": 115.83618000000001, "r": 173.09366, "b": 123.76251000000002, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "TSR and cell detection results compared between OTSL and HTML on", "bbox": {"l": 181.30299, "t": 115.89899000000003, "r": 480.59151999999995, "b": 123.96868999999992, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using Table-", "bbox": {"l": 134.765, "t": 126.85797000000014, "r": 480.59357000000006, "b": 134.92767000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Former [9] (with enc=6, dec=6, heads=8).", "bbox": {"l": 134.765, "t": 137.81696, "r": 305.95691, "b": 145.88666, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Data set", "bbox": {"l": 160.782, "t": 166.55895999999996, "r": 194.99779, "b": 174.62865999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Language", "bbox": {"l": 215.52499000000003, "t": 166.534, "r": 254.04465, "b": 174.6037, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "TEDs", "bbox": {"l": 300.397, "t": 161.07898, "r": 323.99118, "b": 169.14868, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "mAP(0.75)", "bbox": {"l": 370.345, "t": 166.55895999999996, "r": 414.74661, "b": 174.62865999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Inference", "bbox": {"l": 426.737, "t": 161.07898, "r": 463.10830999999996, "b": 169.14868, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "time (secs)", "bbox": {"l": 423.11401, "t": 172.03796, "r": 466.72656, "b": 180.10766999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "simple", "bbox": {"l": 262.41299, "t": 174.03101000000004, "r": 288.0596, "b": 182.10071000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "complex", "bbox": {"l": 296.42899, "t": 174.03101000000004, "r": 329.44687, "b": 182.10071000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "all", "bbox": {"l": 345.03299, "t": 174.03101000000004, "r": 354.75793, "b": 182.10071000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "PubTabNet", "bbox": {"l": 154.53799, "t": 192.85999000000004, "r": 201.24129, "b": 200.92969000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "OTSL", "bbox": {"l": 222.43700000000004, "t": 187.38098000000002, "r": 247.13226000000003, "b": 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"0.922", "bbox": {"l": 302.444, "t": 226.63396999999998, "r": 323.43076, "b": 234.70367, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "0.92", "bbox": {"l": 341.70599, "t": 226.63396999999998, "r": 358.08582, "b": 234.70367, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "0.722", "bbox": {"l": 382.052, "t": 226.63396999999998, "r": 403.03876, "b": 234.70367, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "3.26", "bbox": {"l": 436.73199000000005, "t": 226.63396999999998, "r": 453.11182, "b": 234.70367, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "PubTables-1M", "bbox": {"l": 148.62601, "t": 245.46294999999998, "r": 207.1524, "b": 253.53265, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "OTSL", "bbox": {"l": 222.43700000000004, "t": 239.98297000000002, "r": 247.13226000000003, "b": 248.05267000000003, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "0.987", "bbox": {"l": 264.74399, "t": 239.98297000000002, "r": 285.73074, "b": 248.05267000000003, "coord_origin": "TOPLEFT"}}, 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"TOPLEFT"}}, {"id": 52, "text": "0.966", "bbox": {"l": 339.40302, "t": 252.93499999999995, "r": 360.38977, "b": 261.00469999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "0.889", "bbox": {"l": 382.052, "t": 252.93499999999995, "r": 403.03876, "b": 261.00469999999996, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "3.26", "bbox": {"l": 436.73199000000005, "t": 252.93499999999995, "r": 453.11182, "b": 261.00469999999996, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "5.3", "bbox": {"l": 134.765, "t": 288.91479, "r": 149.40205, "b": 297.72173999999995, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Qualitative Results", "bbox": {"l": 160.85904, "t": 288.91479, "r": 257.08679, "b": 297.72173999999995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5", "bbox": {"l": 134.765, "t": 309.86078, "r": 480.58777, "b": 318.65775, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "demonstrates less overlap and more accurate bounding boxes with OTSL. In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap (E) than the HTML model (D), when predicting the structure of a sparse ta-", "bbox": {"l": 134.765, "t": 408.61190999999997, "r": 480.59106, "b": 416.68167000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "ble (A), at twice the inference speed because of shorter sequence length (B),(C).", "bbox": {"l": 134.765, "t": 419.57089, "r": 480.58838000000003, "b": 427.64066, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\"PMC2807444_006_00.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 430.52987999999993, "r": 304.69171, "b": 438.59964, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "<table>", "bbox": {"l": 180.12473, "t": 516.2332200000001, "r": 190.62042, "b": 518.94992, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "<tr><td></td><td colspan=\"4\"></td><td colspan=\"6\"></td><td colspan=\"3\"></td></tr>", "bbox": {"l": 183.2438, "t": 520.13208, "r": 304.54797, "b": 522.84879, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>", "bbox": {"l": 183.2438, "t": 524.03094, "r": 388.42313, "b": 526.74765, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>", "bbox": {"l": 183.2438, "t": 527.9297799999999, "r": 388.42313, "b": 530.64648, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>", "bbox": {"l": 183.2438, "t": 531.82861, "r": 388.42313, "b": 534.54532, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>", "bbox": {"l": 183.2438, "t": 535.72748, "r": 388.42313, "b": 538.44418, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>", "bbox": {"l": 183.2438, "t": 539.62631, "r": 388.42313, "b": 542.34303, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>", "bbox": {"l": 183.2438, "t": 543.52516, "r": 388.42313, "b": 546.24188, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>", "bbox": {"l": 183.2438, "t": 547.42401, "r": 388.42313, "b": 550.14073, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>", "bbox": {"l": 183.2438, "t": 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bounding boxes with OTSL. In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes."}, {"label": "caption", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 397.59012, "r": 480.59106, "b": 439.71716, "coord_origin": "TOPLEFT"}, "confidence": 0.9482712745666504, "cells": [{"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap (E) than the HTML model (D), when predicting the structure of a sparse ta-", "bbox": {"l": 134.765, "t": 408.61190999999997, "r": 480.59106, "b": 416.68167000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "ble (A), at twice the inference speed because of shorter sequence length (B),(C).", "bbox": {"l": 134.765, "t": 419.57089, "r": 480.58838000000003, "b": 427.64066, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\"PMC2807444_006_00.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 430.52987999999993, "r": 304.69171, "b": 438.59964, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "\u03bc", "bbox": {"l": 342.63354, "t": 430.19678, "r": 344.81915, "b": 439.71716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 5. 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The OTSL model (B) captured repeating pattern of horizontally", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.58823, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 480.5881999999999, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and", "bbox": {"l": 134.765, "t": 158.73895000000005, "r": 480.58838000000003, "b": 166.80864999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 169.69794000000002, "r": 415.84454, "b": 177.76764000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"label": "picture", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 168.39263916015625, "t": 181.96795654296875, "r": 447.35272216796875, "b": 634.003173828125, "coord_origin": "TOPLEFT"}, "confidence": 0.7615750432014465, "cells": [], "children": [{"id": 10, "label": "text", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 15, "text": "A", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 181.89114, "t": 288.35962000000006, "r": 239.23492, "b": 294.2947700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 13, "text": "Repeating pattern of", "bbox": {"l": 181.89114, "t": 288.35962000000006, "r": 239.23492, "b": 294.2947700000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 181.89114, "t": 294.89423, "r": 251.52917, "b": 300.82938, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 14, "text": "horizontally merged cells", "bbox": {"l": 181.89114, "t": 294.89423, "r": 251.52917, "b": 300.82938, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 8, "text": "B", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 172.27777, "t": 381.36288, "r": 180.18666, "b": 403.40067, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 17, "text": "OTSL", "bbox": {"l": 172.27777, "t": 381.36288, "r": 180.18666, "b": 403.40067, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 293.64209, "t": 465.59784, "r": 437.50800000000004, "b": 471.53299, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 12, "text": "Repeating pattern is well represented in predictions", "bbox": {"l": 293.64209, "t": 465.59784, "r": 437.50800000000004, "b": 471.53299, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 9, "text": "C", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 172.27747, "t": 555.7769499999999, "r": 180.18663, "b": 578.7478, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 18, "text": "HTML", "bbox": {"l": 172.27747, "t": 555.7769499999999, "r": 180.18663, "b": 578.7478, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 292.18976, "t": 607.80609, "r": 381.54663, "b": 613.7412400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 16, "text": "Bounding box drifting at the end", "bbox": {"l": 292.18976, "t": 607.80609, "r": 381.54663, "b": 613.7412400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "Horizontally merged cells are not present", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 10, "text": "Incorrect end of HTML sequence", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "caption", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.58838000000003, "b": 177.76764000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8657404184341431, "cells": [{"id": 2, "text": "Fig. 6.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Visualization of predicted structure and detected bounding boxes on a complex", "bbox": {"l": 165.215, "t": 125.86200000000008, "r": 480.58752, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.58823, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 480.5881999999999, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and", "bbox": {"l": 134.765, "t": 158.73895000000005, "r": 480.58838000000003, "b": 166.80864999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 169.69794000000002, "r": 415.84454, "b": 177.76764000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"label": "picture", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 168.39263916015625, "t": 181.96795654296875, "r": 447.35272216796875, "b": 634.003173828125, "coord_origin": "TOPLEFT"}, "confidence": 0.7615750432014465, "cells": [], "children": [{"id": 10, "label": "text", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 15, "text": "A", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 181.89114, "t": 288.35962000000006, "r": 239.23492, "b": 294.2947700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 13, "text": "Repeating 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"t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9301635026931763, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "page_header", "bbox": {"l": 471.37561, "t": 93.77099999999996, "r": 480.5894799999999, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9007187485694885, "cells": [{"id": 1, "text": "11", "bbox": {"l": 471.37561, "t": 93.77099999999996, "r": 480.5894799999999, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8610868453979492, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8927640914916992, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156443476676941, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806177020072937, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868756532669067, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403368830680847, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698705077171326, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 139.37097, "t": 567.51884, "r": 480.5920100000001, "b": 608.46561, "coord_origin": "TOPLEFT"}, "confidence": 0.973068118095398, "cells": [{"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 480.58731000000006, "b": 631.18761, "coord_origin": "TOPLEFT"}, "confidence": 0.9617277979850769, "cells": [{"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 139.37097, "t": 634.88084, "r": 480.58826, "b": 664.86761, "coord_origin": "TOPLEFT"}, "confidence": 0.975471019744873, "cells": [{"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8610868453979492, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}, {"label": "page_header", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8927640914916992, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156443476676941, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806177020072937, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868756532669067, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403368830680847, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698705077171326, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. 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IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)"}], "body": [{"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156443476676941, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806177020072937, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868756532669067, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. 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Pattern Recognition", "bbox": {"l": 151.51797, "t": 207.34491000000003, "r": 318.55124, "b": 215.41461000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "126", "bbox": {"l": 321.62097, "t": 207.2821, "r": 337.53186, "b": 215.20844, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ", 108565 (2022)", "bbox": {"l": 337.53296, "t": 207.34491000000003, "r": 399.46927, "b": 215.41461000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "20. Zhang, Z., Zhang, J., Du, J., Wang, F.: Split, embed and merge: An accurate table structure recognizer. Pattern Recognition 126 , 108565 (2022)"}, {"label": "list_item", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 134.76495, "t": 218.30389000000002, "r": 480.59305000000006, "b": 270.25438999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9720271825790405, "cells": [{"id": 19, "text": "21.", "bbox": {"l": 134.76495, "t": 218.30389000000002, "r": 145.7213, "b": 226.3736, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Zheng, X., Burdick, D., Popa, L., Zhong, X., Wang, N.X.R.: Global table extractor", "bbox": {"l": 150.00871, "t": 218.30389000000002, "r": 480.59012, "b": 226.3736, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "(gte): A framework for joint table identification and cell structure recognition using", "bbox": {"l": 151.51796, "t": 229.26288, "r": 480.59102999999993, "b": 237.33258, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "visual context. In: 2021 IEEE Winter Conference on Applications of Computer Vi-", "bbox": {"l": 151.51796, "t": 240.22186, "r": 480.59119, "b": 248.29156, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "sion (WACV). pp. 697-706 (2021).", "bbox": {"l": 151.51796, "t": 251.18084999999996, "r": 293.44086, "b": 259.25055, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "https://doi.org/10.1109/WACV48630.2021.", "bbox": {"l": 297.04996, "t": 251.82641999999998, "r": 480.59305000000006, "b": 259.29540999999995, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "00074", "bbox": {"l": 151.51796, "t": 262.7854, "r": 175.05028, "b": 270.25438999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "21. Zheng, X., Burdick, D., Popa, L., Zhong, X., Wang, N.X.R.: Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 697-706 (2021). https://doi.org/10.1109/WACV48630.2021. 00074"}, {"label": "list_item", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 134.76495, "t": 273.09882000000005, "r": 480.59558, "b": 314.0455, "coord_origin": "TOPLEFT"}, "confidence": 0.9685125946998596, "cells": [{"id": 26, "text": "22.", "bbox": {"l": 134.76495, "t": 273.09882000000005, "r": 146.36798, "b": 281.16855000000004, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition:", "bbox": {"l": 150.90846, "t": 273.09882000000005, "r": 480.59094, "b": 281.16855000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Data, model, and evaluation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M.", "bbox": {"l": 151.51796, "t": 284.05777, "r": 480.58832000000007, "b": 292.12753, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "(eds.) Computer Vision - ECCV 2020. pp. 564-580. Springer International Pub-", "bbox": {"l": 151.51796, "t": 295.01675, "r": 480.59558, "b": 303.08651999999995, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "lishing, Cham (2020)", "bbox": {"l": 151.51796, "t": 305.97574, "r": 236.02359, "b": 314.0455, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "22. Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition: Data, model, and evaluation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision - ECCV 2020. pp. 564-580. Springer International Publishing, Cham (2020)"}, {"label": "list_item", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 134.76495, "t": 316.93472, "r": 480.59454, "b": 346.92148, "coord_origin": "TOPLEFT"}, "confidence": 0.9668972492218018, "cells": [{"id": 31, "text": "23.", "bbox": {"l": 134.76495, "t": 316.93472, "r": 145.69547, "b": 325.00449000000003, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document lay-", "bbox": {"l": 149.97276, "t": 316.93472, "r": 480.59454, "b": 325.00449000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "out analysis. In: 2019 International Conference on Document Analysis and Recog-", "bbox": {"l": 151.51796, "t": 327.8927299999999, "r": 480.59387000000004, "b": 335.96249, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "nition (ICDAR). pp. 1015-1022. IEEE (2019)", "bbox": {"l": 151.51796, "t": 338.85172, "r": 335.13635, "b": 346.92148, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "23. Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1015-1022. IEEE (2019)"}], "headers": [{"label": "page_header", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7012730240821838, "cells": [{"id": 0, "text": "14", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "14"}, {"label": "page_header", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7889755368232727, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}]
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/redp5110_sampled.doctags.txt b/tests/data/groundtruth/docling_v1/redp5110_sampled.doctags.txt
index 1fae16d3..20c8352b 100644
--- a/tests/data/groundtruth/docling_v1/redp5110_sampled.doctags.txt
+++ b/tests/data/groundtruth/docling_v1/redp5110_sampled.doctags.txt
@@ -99,7 +99,7 @@
 <paragraph><location><page_8><loc_22><loc_66><loc_85><loc_69></location>The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view.</paragraph>
 <caption><location><page_8><loc_22><loc_64><loc_46><loc_65></location>Table 2-1 FUNCTION_USAGE view</caption>
 <table>
-<location><page_8><loc_23><loc_45><loc_88><loc_63></location>
+<location><page_8><loc_22><loc_44><loc_89><loc_63></location>
 <caption>Table 2-1 FUNCTION_USAGE view</caption>
 <row_0><col_0><col_header>Column name</col_0><col_1><col_header>Data type</col_1><col_2><col_header>Description</col_2></row_0>
 <row_1><col_0><body>FUNCTION_ID</col_0><col_1><body>VARCHAR(30)</col_1><col_2><body>ID of the function.</col_2></row_1>
@@ -130,21 +130,21 @@
 <paragraph><location><page_9><loc_22><loc_53><loc_89><loc_56></location>Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools.</paragraph>
 <caption><location><page_9><loc_11><loc_51><loc_64><loc_52></location>Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority</caption>
 <table>
-<location><page_9><loc_12><loc_10><loc_88><loc_49></location>
+<location><page_9><loc_11><loc_9><loc_89><loc_50></location>
 <caption>Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority</caption>
-<row_0><col_0><body>User action</col_0><col_1><body>*JOBCTL</col_1><col_2><body>QIBM_DB_SECADM</col_2><col_3><body>QIBM_DB_SQLADM</col_3><col_4><body>QIBM_DB_SYSMON No Authority</col_4></row_0>
-<row_1><col_0><row_header>SET CURRENT DEGREE  (SQL statement)</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_1>
-<row_2><col_0><row_header>CHGQRYA  command targeting a different user's job</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_2>
-<row_3><col_0><row_header>STRDBMON  or  ENDDBMON  commands targeting a different user's job</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_3>
-<row_4><col_0><row_header>STRDBMON  or  ENDDBMON  commands targeting a job that matches the current user</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body>X X</col_4></row_4>
-<row_5><col_0><row_header>QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body>X</col_4></row_5>
-<row_6><col_0><row_header>Visual Explain within Run SQL scripts</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body>X X</col_4></row_6>
-<row_7><col_0><row_header>Visual Explain outside of Run SQL scripts</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_7>
-<row_8><col_0><row_header>ANALYZE PLAN CACHE procedure</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_8>
-<row_9><col_0><row_header>DUMP PLAN CACHE procedure</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_9>
-<row_10><col_0><row_header>MODIFY PLAN CACHE procedure</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_10>
-<row_11><col_0><row_header>MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority)</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_11>
-<row_12><col_0><row_header>CHANGE PLAN CACHE SIZE procedure (currently does not check authority)</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_12>
+<row_0><col_0><row_header>User action</col_0><col_1><body>*JOBCTL</col_1><col_2><body>QIBM_DB_SECADM</col_2><col_3><body>QIBM_DB_SQLADM</col_3><col_4><body>QIBM_DB_SYSMON</col_4><col_5><body>No Authority</col_5></row_0>
+<row_1><col_0><row_header>SET CURRENT DEGREE  (SQL statement)</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_1>
+<row_2><col_0><row_header>CHGQRYA  command targeting a different user's job</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_2>
+<row_3><col_0><row_header>STRDBMON  or  ENDDBMON  commands targeting a different user's job</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_3>
+<row_4><col_0><row_header>STRDBMON  or  ENDDBMON  commands targeting a job that matches the current user</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body>X</col_4><col_5><body>X</col_5></row_4>
+<row_5><col_0><row_header>QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body>X</col_4><col_5><body></col_5></row_5>
+<row_6><col_0><row_header>Visual Explain within Run SQL scripts</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body>X</col_4><col_5><body>X</col_5></row_6>
+<row_7><col_0><row_header>Visual Explain outside of Run SQL scripts</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_7>
+<row_8><col_0><row_header>ANALYZE PLAN CACHE procedure</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_8>
+<row_9><col_0><row_header>DUMP PLAN CACHE procedure</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_9>
+<row_10><col_0><row_header>MODIFY PLAN CACHE procedure</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_10>
+<row_11><col_0><row_header>MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority)</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_11>
+<row_12><col_0><row_header>CHANGE PLAN CACHE SIZE procedure (currently does not check authority)</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_12>
 </table>
 <caption><location><page_10><loc_22><loc_88><loc_86><loc_91></location>The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.</caption>
 <caption><location><page_10><loc_22><loc_47><loc_56><loc_48></location>Figure 3-1 CREATE PERMISSION SQL statement</caption>
@@ -157,7 +157,7 @@
 <paragraph><location><page_11><loc_22><loc_90><loc_67><loc_91></location>Table 3-1 summarizes these special registers and their values.</paragraph>
 <caption><location><page_11><loc_22><loc_87><loc_61><loc_88></location>Table 3-1 Special registers and their corresponding values</caption>
 <table>
-<location><page_11><loc_23><loc_75><loc_88><loc_86></location>
+<location><page_11><loc_22><loc_74><loc_89><loc_87></location>
 <caption>Table 3-1 Special registers and their corresponding values</caption>
 <row_0><col_0><col_header>Special register</col_0><col_1><col_header>Corresponding value</col_1></row_0>
 <row_1><col_0><body>USER or SESSION_USER</col_0><col_1><body>The effective user of the thread excluding adopted authority.</col_1></row_1>
@@ -181,7 +181,7 @@
 <paragraph><location><page_12><loc_22><loc_90><loc_56><loc_91></location>Table 3-2 lists the nine built-in global variables.</paragraph>
 <caption><location><page_12><loc_11><loc_87><loc_33><loc_88></location>Table 3-2 Built-in global variables</caption>
 <table>
-<location><page_12><loc_12><loc_63><loc_86><loc_86></location>
+<location><page_12><loc_10><loc_63><loc_90><loc_87></location>
 <caption>Table 3-2 Built-in global variables</caption>
 <row_0><col_0><col_header>Global variable</col_0><col_1><col_header>Type</col_1><col_2><col_header>Description</col_2></row_0>
 <row_1><col_0><body>CLIENT_HOST</col_0><col_1><body>VARCHAR(255)</col_1><col_2><body>Host name of the current client as returned by the system</col_2></row_1>
diff --git a/tests/data/groundtruth/docling_v1/redp5110_sampled.json b/tests/data/groundtruth/docling_v1/redp5110_sampled.json
index 0e5f6405..846ad7f0 100644
--- a/tests/data/groundtruth/docling_v1/redp5110_sampled.json
+++ b/tests/data/groundtruth/docling_v1/redp5110_sampled.json
@@ -1 +1 @@
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All rights reserved.", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [538.8599853515625, 27.93828010559082, 547.25927734375, 37.15127944946289], "page": 2, "span": [0, 3], "__ref_s3_data": null}], "text": "iii", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 706.416015625, 235.86239624023438, 717.5160522460938], "page": 3, "span": [0, 30], "__ref_s3_data": null}], "text": "DB2 for i Center of Excellence", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [94.13269805908203, 636.66357421875, 233.99972534179688, 653.5498657226562], "page": 3, "span": [0, 52], "__ref_s3_data": null}], "text": "Solution Brief IBM Systems Lab Services and Training", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [144.8892059326172, 455.1859436035156, 188.74681091308594, 464.5383605957031], "page": 3, "span": [0, 10], "__ref_s3_data": null}], "text": "Highlights", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [144.8892059326172, 433.3105773925781, 242.87388610839844, 446.7829284667969], "page": 3, "span": [0, 532], "__ref_s3_data": null}], "text": "- GLYPH<g115>GLYPH<g3> GLYPH<g40>GLYPH<g81>GLYPH<g75>GLYPH<g68>GLYPH<g81>GLYPH<g70>GLYPH<g72>GLYPH<g3> GLYPH<g87>GLYPH<g75>GLYPH<g72>GLYPH<g3> GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g73>GLYPH<g82>GLYPH<g85>GLYPH<g80>GLYPH<g68>GLYPH<g81>GLYPH<g70>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g92>GLYPH<g82>GLYPH<g88>GLYPH<g85> GLYPH<g3> GLYPH<g71>GLYPH<g68>GLYPH<g87>GLYPH<g68>GLYPH<g69>GLYPH<g68>GLYPH<g86>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g68>GLYPH<g87>GLYPH<g76>GLYPH<g82>GLYPH<g81>GLYPH<g86>", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [144.8892059326172, 402.7626953125, 259.22869873046875, 424.06781005859375], "page": 3, "span": [0, 876], "__ref_s3_data": null}], "text": "- GLYPH<g115>GLYPH<g3> GLYPH<g40>GLYPH<g68>GLYPH<g85> GLYPH<g81>GLYPH<g3> GLYPH<g74>GLYPH<g85>GLYPH<g72>GLYPH<g68>GLYPH<g87>GLYPH<g72>GLYPH<g85>GLYPH<g3> GLYPH<g85>GLYPH<g72>GLYPH<g87>GLYPH<g88>GLYPH<g85> GLYPH<g81>GLYPH<g3> GLYPH<g82>GLYPH<g81>GLYPH<g3> GLYPH<g44>GLYPH<g55>GLYPH<g3> GLYPH<g83>GLYPH<g85>GLYPH<g82>GLYPH<g77>GLYPH<g72>GLYPH<g70>GLYPH<g87>GLYPH<g86> GLYPH<g3> GLYPH<g87>GLYPH<g75>GLYPH<g85>GLYPH<g82>GLYPH<g88>GLYPH<g74>GLYPH<g75>GLYPH<g3> GLYPH<g80>GLYPH<g82>GLYPH<g71>GLYPH<g72>GLYPH<g85> GLYPH<g81>GLYPH<g76>GLYPH<g93>GLYPH<g68>GLYPH<g87>GLYPH<g76>GLYPH<g82>GLYPH<g81>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g71>GLYPH<g68>GLYPH<g87>GLYPH<g68>GLYPH<g69>GLYPH<g68>GLYPH<g86>GLYPH<g72>GLYPH<g3> GLYPH<g68>GLYPH<g81>GLYPH<g71> GLYPH<g3> GLYPH<g68>GLYPH<g83>GLYPH<g83>GLYPH<g79>GLYPH<g76>GLYPH<g70>GLYPH<g68>GLYPH<g87>GLYPH<g76>GLYPH<g82>GLYPH<g81>GLYPH<g86>", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [144.8892059326172, 380.0474548339844, 249.8356170654297, 393.5198059082031], "page": 3, "span": [0, 672], "__ref_s3_data": null}], "text": "- GLYPH<g115>GLYPH<g3> GLYPH<g53>GLYPH<g72>GLYPH<g79>GLYPH<g92>GLYPH<g3> GLYPH<g82>GLYPH<g81>GLYPH<g3> GLYPH<g44>GLYPH<g37>GLYPH<g48>GLYPH<g3> GLYPH<g72>GLYPH<g91>GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g87>GLYPH<g3> GLYPH<g70>GLYPH<g82>GLYPH<g81>GLYPH<g86>GLYPH<g88>GLYPH<g79>GLYPH<g87>GLYPH<g76>GLYPH<g81>GLYPH<g74>GLYPH<g15>GLYPH<g3> GLYPH<g86>GLYPH<g78>GLYPH<g76>GLYPH<g79>GLYPH<g79>GLYPH<g86> GLYPH<g3> GLYPH<g86>GLYPH<g75>GLYPH<g68>GLYPH<g85>GLYPH<g76>GLYPH<g81>GLYPH<g74>GLYPH<g3> GLYPH<g68>GLYPH<g81>GLYPH<g71>GLYPH<g3> GLYPH<g85>GLYPH<g72>GLYPH<g81>GLYPH<g82>GLYPH<g90>GLYPH<g81>GLYPH<g3> GLYPH<g86>GLYPH<g72>GLYPH<g85>GLYPH<g89>GLYPH<g76>GLYPH<g70>GLYPH<g72>GLYPH<g86>", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [144.8892059326172, 357.3323669433594, 234.2516326904297, 370.8047180175781], "page": 3, "span": [0, 613], "__ref_s3_data": null}], "text": "- GLYPH<g115>GLYPH<g3> GLYPH<g55> GLYPH<g68>GLYPH<g78>GLYPH<g72>GLYPH<g3> GLYPH<g68>GLYPH<g71>GLYPH<g89>GLYPH<g68>GLYPH<g81>GLYPH<g87>GLYPH<g68>GLYPH<g74>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g68>GLYPH<g70>GLYPH<g70>GLYPH<g72>GLYPH<g86>GLYPH<g86>GLYPH<g3> GLYPH<g87>GLYPH<g82>GLYPH<g3> GLYPH<g68> GLYPH<g3> GLYPH<g90>GLYPH<g82>GLYPH<g85>GLYPH<g79>GLYPH<g71>GLYPH<g90>GLYPH<g76>GLYPH<g71>GLYPH<g72>GLYPH<g3> GLYPH<g86>GLYPH<g82>GLYPH<g88>GLYPH<g85>GLYPH<g70>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g72>GLYPH<g91>GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g87>GLYPH<g76>GLYPH<g86>GLYPH<g72>", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [461.0885925292969, 646.5781860351562, 506.26177978515625, 653.5924682617188], "page": 3, "span": [0, 14], "__ref_s3_data": null}], "text": "Power Services", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 515.3794555664062, 463.8094177246094, 552.6573486328125], "page": 3, "span": [0, 30], "__ref_s3_data": null}], "text": "DB2 for i Center of Excellence", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [280.2401123046875, 504.5404052734375, 483.29571533203125, 514.4097290039062], "page": 3, "span": [0, 49], "__ref_s3_data": null}], "text": "Expert help to achieve your business requirements", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 467.1043395996094, 443.2821044921875, 476.1183776855469], "page": 3, "span": [0, 37], "__ref_s3_data": null}], "text": "We build confident, satisfied clients", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [280.2401123046875, 447.0404968261719, 488.1546630859375, 464.6240539550781], "page": 3, "span": [0, 122], "__ref_s3_data": null}], "text": "No one else has the vast consulting experiences, skills sharing and renown service offerings to do what we can do for you.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 427.2699890136719, 367.8602294921875, 434.6739807128906], "page": 3, "span": [0, 27], "__ref_s3_data": null}], "text": "Because no one else is IBM.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 366.77972412109375, 500.321044921875, 414.9019775390625], "page": 3, "span": [0, 318], "__ref_s3_data": null}], "text": "With combined experiences and direct access to development groups, we're the experts in IBM DB2\u00ae for i. The DB2 for i Center of Excellence (CoE) can help you achieve-perhaps reexamine and exceed-your business requirements and gain more confidence and satisfaction in IBM product data management products and solutions.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 345.1319274902344, 434.8320617675781, 354.1459655761719], "page": 3, "span": [0, 30], "__ref_s3_data": null}], "text": "Who we are, some of what we do", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [280.2401123046875, 335.2477722167969, 434.56317138671875, 342.6517639160156], "page": 3, "span": [0, 46], "__ref_s3_data": null}], "text": "Global CoE engagements cover topics including:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 315.4777526855469, 401.5641174316406, 322.8817443847656], "page": 3, "span": [0, 38], "__ref_s3_data": null}], "text": "- r Database performance and scalability", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 305.2950439453125, 424.9964599609375, 312.69903564453125], "page": 3, "span": [0, 44], "__ref_s3_data": null}], "text": "- r Advanced SQL knowledge and skills transfer", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 295.1124572753906, 392.158447265625, 302.5164489746094], "page": 3, "span": [0, 37], "__ref_s3_data": null}], "text": "- r Business intelligence and analytics", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 284.92974853515625, 339.94354248046875, 292.333740234375], "page": 3, "span": [0, 15], "__ref_s3_data": null}], "text": "- r DB2 Web Query", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 274.7471618652344, 504.1931457519531, 282.1511535644531], "page": 3, "span": [0, 72], "__ref_s3_data": null}], "text": "- r Query/400 modernization for better reporting and analysis capabilities", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 264.564453125, 423.002197265625, 271.96844482421875], "page": 3, "span": [0, 43], "__ref_s3_data": null}], "text": "- r Database modernization and re-engineering", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 254.38186645507812, 399.6517333984375, 261.7858581542969], "page": 3, "span": [0, 38], "__ref_s3_data": null}], "text": "- r Data-centric architecture and design", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 244.1992645263672, 466.77880859375, 251.60325622558594], "page": 3, "span": [0, 58], "__ref_s3_data": null}], "text": "- r Extremely large database and overcoming limits to growth", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 234.0165557861328, 382.2095642089844, 241.42054748535156], "page": 3, "span": [0, 30], "__ref_s3_data": null}], "text": "- r ISV education and enablement", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.80000305175781, 695.9519653320312, 151.46160888671875, 718.1519775390625], "page": 4, "span": [0, 7], "__ref_s3_data": null}], "text": "Preface", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.79983520507812, 590.1392822265625, 547.3082275390625, 659.3513793945312], "page": 4, "span": [0, 469], "__ref_s3_data": null}], "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79986572265625, 532.1800537109375, 546.4656982421875, 577.3925170898438], "page": 4, "span": [0, 309], "__ref_s3_data": null}], "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 450.1584777832031, 547.2366943359375, 471.37127685546875], "page": 4, "span": [0, 172], "__ref_s3_data": null}], "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [263.3995666503906, 275.1402587890625, 541.2507934570312, 416.3512268066406], "page": 4, "span": [0, 684], "__ref_s3_data": null}], "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/6"}, {"prov": [{"bbox": [64.80000305175781, 28.136999130249023, 257.24334716796875, 36.461997985839844], "page": 4, "span": [0, 48], "__ref_s3_data": null}], "text": "' Copyright IBM Corp. 2014. All rights reserved.", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [263.39959716796875, 111.162841796875, 541.2737426757812, 264.37347412109375], "page": 4, "span": [0, 726], "__ref_s3_data": null}], "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [538.8599853515625, 27.93828010559082, 547.2503051757812, 37.15127944946289], "page": 4, "span": [0, 2], "__ref_s3_data": null}], "text": "xi", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 488.9364013671875, 125.36660766601562, 503.69940185546875], "page": 4, "span": [0, 7], "__ref_s3_data": null}], "text": "Authors", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/7"}, {"prov": [{"bbox": [81.0, 517.019287109375, 115.13253021240234, 523.457275390625], "page": 5, "span": [0, 10], "__ref_s3_data": null}], "text": "Chapter 1.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [500.3999938964844, 661.8682861328125, 522.6177368164062, 698.831298828125], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "1", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 482.1217956542969, 547.3047485351562, 537.1136474609375], "page": 5, "span": [0, 36], "__ref_s3_data": null}], "text": "Securing and protecting IBM DB2 data", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.79965209960938, 362.078857421875, 547.2540283203125, 443.2912902832031], "page": 5, "span": [0, 648], "__ref_s3_data": null}], "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80023193359375, 304.0598449707031, 527.206298828125, 349.27227783203125], "page": 5, "span": [0, 304], "__ref_s3_data": null}], "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8002471923828, 270.1002197265625, 547.1551513671875, 291.3130187988281], "page": 5, "span": [0, 122], "__ref_s3_data": null}], "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8002471923828, 253.06063842773438, 250.23167419433594, 262.2736511230469], "page": 5, "span": [0, 37], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Security fundamentals", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8002471923828, 241.0608367919922, 282.98114013671875, 250.27383422851562], "page": 5, "span": [0, 47], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Current state of IBM i security", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8002471923828, 229.06103515625, 264.8818664550781, 238.27403259277344], "page": 5, "span": [0, 43], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> DB2 for i security controls", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 67.21955871582031, 258.362548828125, 74.24993896484375], "page": 5, "span": [0, 35], "__ref_s3_data": null}], "text": "$^{1 }$http://www.idtheftcenter.org", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [136.8000030517578, 57.02824020385742, 234.05880737304688, 64.40973663330078], "page": 5, "span": [0, 31], "__ref_s3_data": null}], "text": "$^{2 }$http://www.ponemon.org /", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [64.80000305175781, 28.136999130249023, 257.24334716796875, 36.461997985839844], "page": 5, "span": [0, 48], "__ref_s3_data": null}], "text": "' Copyright IBM Corp. 2014. All rights reserved.", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [541.6798706054688, 27.93828010559082, 547.2176513671875, 37.15127944946289], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "1", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 702.8963012695312, 267.40582275390625, 717.6593017578125], "page": 6, "span": [0, 25], "__ref_s3_data": null}], "text": "1.1 Security fundamentals", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 664.178466796875, 545.0048217773438, 685.3912963867188], "page": 6, "span": [0, 133], "__ref_s3_data": null}], "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 611.138916015625, 547.1642456054688, 656.8751220703125], "page": 6, "span": [0, 361], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.199462890625, 522.1602172851562, 547.2608642578125, 603.3721313476562], "page": 6, "span": [0, 587], "__ref_s3_data": null}], "text": "- The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.199462890625, 505.180419921875, 541.9920043945312, 514.3934326171875], "page": 6, "span": [0, 90], "__ref_s3_data": null}], "text": "A security policy is what defines whether the system and its settings are secure (or not).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79930114746094, 416.139404296875, 547.1582641601562, 497.8750305175781], "page": 6, "span": [0, 573], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8002166748047, 382.1797790527344, 535.3616943359375, 403.392578125], "page": 6, "span": [0, 179], "__ref_s3_data": null}], "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 338.936279296875, 323.3839111328125, 353.69927978515625], "page": 6, "span": [0, 35], "__ref_s3_data": null}], "text": "1.2 Current state of IBM i security", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 276.1588439941406, 547.3182373046875, 321.37127685546875], "page": 6, "span": [0, 306], "__ref_s3_data": null}], "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 206.1400604248047, 547.284423828125, 263.3522644042969], "page": 6, "span": [0, 405], "__ref_s3_data": null}], "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 112.12167358398438, 547.2832641601562, 193.33349609375], "page": 6, "span": [0, 640], "__ref_s3_data": null}], "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 72.8219985961914, 37.15127944946289], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "2", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [87.84030151367188, 28.136999130249023, 328.7253723144531, 36.461997985839844], "page": 6, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 72.8219985961914, 37.15127944946289], "page": 7, "span": [0, 1], "__ref_s3_data": null}], "text": "4", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [87.84030151367188, 28.136999130249023, 328.7253723144531, 36.461997985839844], "page": 7, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.8000030517578, 639.2794189453125, 544.3033447265625, 720.4913330078125], "page": 7, "span": [0, 589], "__ref_s3_data": null}], "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 606.67724609375, 301.4690246582031, 618.665283203125], "page": 7, "span": [0, 37], "__ref_s3_data": null}], "text": "1.3.1 Existing row and column control", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.79998779296875, 535.2990112304688, 541.5673828125, 592.5112915039062], "page": 7, "span": [0, 377], "__ref_s3_data": null}], "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79998779296875, 477.27996826171875, 547.4407958984375, 522.492431640625], "page": 7, "span": [0, 340], "__ref_s3_data": null}], "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79998779296875, 431.2607727050781, 547.232666015625, 464.473388671875], "page": 7, "span": [0, 247], "__ref_s3_data": null}], "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 91.85700225830078, 316.447265625, 100.18199920654297], "page": 7, "span": [0, 43], "__ref_s3_data": null}], "text": "Figure 1-2 Existing row and column controls", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/8"}, {"prov": [{"bbox": [64.80000305175781, 708.67724609375, 335.4955139160156, 720.665283203125], "page": 8, "span": [0, 38], "__ref_s3_data": null}], "text": "2.1.6 Change Function Usage CL command", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 685.2982788085938, 547.284423828125, 694.5112915039062], "page": 8, "span": [0, 90], "__ref_s3_data": null}], "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 668.2587280273438, 301.5174865722656, 677.4717407226562], "page": 8, "span": [0, 49], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.80099487304688, 656.2589111328125, 313.39776611328125, 665.471923828125], "page": 8, "span": [0, 51], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8009796142578, 644.2590942382812, 310.8171081542969, 653.4721069335938], "page": 8, "span": [0, 52], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.7999725341797, 610.2994995117188, 512.5380249023438, 631.5123291015625], "page": 8, "span": [0, 126], "__ref_s3_data": null}], "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80096435546875, 593.5487670898438, 441.59686279296875, 602.3235473632812], "page": 8, "span": [0, 61], "__ref_s3_data": null}], "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 560.6572875976562, 544.4754638671875, 572.6453247070312], "page": 8, "span": [0, 72], "__ref_s3_data": null}], "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 525.2785034179688, 519.5179443359375, 546.4913330078125], "page": 8, "span": [0, 130], "__ref_s3_data": null}], "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 504.11700439453125, 283.9680480957031, 512.4420166015625], "page": 8, "span": [0, 29], "__ref_s3_data": null}], "text": "Table 2-1 FUNCTION_USAGE view", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [136.8000030517578, 318.2784729003906, 547.2803955078125, 339.49127197265625], "page": 8, "span": [0, 112], "__ref_s3_data": null}], "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 297.11700439453125, 462.35418701171875, 305.4420166015625], "page": 8, "span": [0, 74], "__ref_s3_data": null}], "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "type": "paragraph", "payload": null, "name": "paragraph", "font": null}, {"prov": [{"bbox": [136.8, 279.56719999999996, 171.26956, 288.34198], "page": 8, "span": [0, 6], "__ref_s3_data": null}], "text": "SELECT", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [182.75941, 279.56719999999996, 251.69853, 288.34198], "page": 8, "span": [0, 12], "__ref_s3_data": null}], "text": "function_id,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [166.78244, 267.56737999999996, 241.73852999999997, 276.3421599999999], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "user_name,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [170.75961, 255.56758000000002, 221.69901999999996, 264.34235], "page": 8, "span": [0, 6], "__ref_s3_data": null}], "text": "usage,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [167.53809, 243.56777999999997, 236.69878, 252.34253], "page": 8, "span": [0, 9], "__ref_s3_data": null}], "text": "user_type", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 231.56798000000003, 160.59396, 240.34272999999996], "page": 8, "span": [0, 4], "__ref_s3_data": null}], "text": "FROM", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [178.43944, 231.56798000000003, 261.71829, 240.34272999999996], "page": 8, "span": [0, 14], "__ref_s3_data": null}], "text": "function_usage", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 219.56817999999998, 162.44176, 228.34293000000002], "page": 8, "span": [0, 5], "__ref_s3_data": null}], "text": "WHERE", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [177.8268, 219.56817999999998, 331.67731, 228.34293000000002], "page": 8, "span": [0, 28], "__ref_s3_data": null}], "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 207.56836999999996, 178.77542, 216.34312], "page": 8, "span": [0, 8], "__ref_s3_data": null}], "text": "ORDER BY", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [189.26929, 207.56836999999996, 241.73856, 216.34312], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "user_name;", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 157.01637268066406, 249.59605407714844, 171.7793731689453], "page": 8, "span": [0, 24], "__ref_s3_data": null}], "text": "2.2 Separation of duties", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 82.23904418945312, 547.2234497070312, 139.45127868652344], "page": 8, "span": [0, 463], "__ref_s3_data": null}], "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 78.4020004272461, 37.15127944946289], "page": 8, "span": [0, 2], "__ref_s3_data": null}], "text": "10", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [93.42030334472656, 28.136999130249023, 334.4214172363281, 36.461997985839844], "page": 8, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.79959106445312, 651.2788696289062, 542.6943359375, 720.490966796875], "page": 9, "span": [0, 516], "__ref_s3_data": null}], "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 593.2598266601562, 547.303955078125, 638.4722900390625], "page": 9, "span": [0, 285], "__ref_s3_data": null}], "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 559.3002319335938, 538.6507568359375, 580.5130615234375], "page": 9, "span": [0, 129], "__ref_s3_data": null}], "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 513.281005859375, 545.7960205078125, 546.49365234375], "page": 9, "span": [0, 204], "__ref_s3_data": null}], "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 455.2619934082031, 539.80712890625, 500.47442626953125], "page": 9, "span": [0, 285], "__ref_s3_data": null}], "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 421.3023681640625, 543.067138671875, 442.5151672363281], "page": 9, "span": [0, 136], "__ref_s3_data": null}], "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 400.1369934082031, 391.754638671875, 408.4620056152344], "page": 9, "span": [0, 78], "__ref_s3_data": null}], "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"prov": [{"bbox": [355.32000732421875, 28.136999130249023, 523.5407104492188, 36.461997985839844], "page": 9, "span": [0, 41], "__ref_s3_data": null}], "text": "Chapter 2. Roles and separation of duties", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 9, "span": [0, 2], "__ref_s3_data": null}], "text": "11", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.799560546875, 699.2781372070312, 528.7305908203125, 720.490966796875], "page": 10, "span": [0, 135], "__ref_s3_data": null}], "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [136.8000030517578, 369.5369873046875, 341.9765930175781, 377.86199951171875], "page": 10, "span": [0, 42], "__ref_s3_data": null}], "text": "Figure 3-1 CREATE PERMISSION SQL statement", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/9"}, {"prov": [{"bbox": [136.8000030517578, 340.95599365234375, 215.37600708007812, 352.0559997558594], "page": 10, "span": [0, 11], "__ref_s3_data": null}], "text": "Column mask", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 291.6988525390625, 542.7664794921875, 336.9112854003906], "page": 10, "span": [0, 297], "__ref_s3_data": null}], "text": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [344.94000244140625, 28.136999130249023, 523.6016235351562, 36.461997985839844], "page": 10, "span": [0, 40], "__ref_s3_data": null}], "text": "Chapter 3. Row and Column Access Control", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 10, "span": [0, 2], "__ref_s3_data": null}], "text": "15", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.79959106445312, 711.2779541015625, 412.20758056640625, 720.490966796875], "page": 11, "span": [0, 62], "__ref_s3_data": null}], "text": "Table 3-1 summarizes these special registers and their values.", "type": "paragraph", "payload": null, "name": "paragraph", "font": null}, {"prov": [{"bbox": [136.8000030517578, 690.177001953125, 372.6036376953125, 698.501953125], "page": 11, "span": [0, 58], "__ref_s3_data": null}], "text": "Table 3-1 Special registers and their corresponding values", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/3"}, {"prov": [{"bbox": [136.8000030517578, 556.2984619140625, 538.493896484375, 577.5112915039062], "page": 11, "span": [0, 97], "__ref_s3_data": null}], "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 539.2589111328125, 411.36138916015625, 548.471923828125], "page": 11, "span": [0, 75], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> A user connects to the server using the user profile ALICE.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 522.2791137695312, 453.2580871582031, 531.4921264648438], "page": 11, "span": [0, 77], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 493.29949951171875, 541.4498291015625, 514.5123291015625], "page": 11, "span": [0, 160], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 452.2602844238281, 547.2167358398438, 485.472900390625], "page": 11, "span": [0, 253], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.80101013183594, 423.2806701660156, 547.3540649414062, 444.49346923828125], "page": 11, "span": [0, 133], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 186.95709228515625, 341.2566223144531, 195.2821044921875], "page": 11, "span": [0, 50], "__ref_s3_data": null}], "text": "Figure 3-5 Special registers and adopted authority", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/10"}, {"prov": [{"bbox": [64.80000305175781, 154.457275390625, 247.02536010742188, 166.44528198242188], "page": 11, "span": [0, 31], "__ref_s3_data": null}], "text": "3.2.2 Built-in global variables", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 119.0784683227539, 518.0011596679688, 140.29127502441406], "page": 11, "span": [0, 161], "__ref_s3_data": null}], "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 73.05928039550781, 532.3385009765625, 106.27189636230469], "page": 11, "span": [0, 233], "__ref_s3_data": null}], "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [344.94000244140625, 28.136999130249023, 523.6016235351562, 36.461997985839844], "page": 11, "span": [0, 40], "__ref_s3_data": null}], "text": "Chapter 3. Row and Column Access Control", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 11, "span": [0, 2], "__ref_s3_data": null}], "text": "19", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 78.4020004272461, 37.15127944946289], "page": 12, "span": [0, 2], "__ref_s3_data": null}], "text": "20", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [93.42030334472656, 28.136999130249023, 334.4214172363281, 36.461997985839844], "page": 12, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.8000030517578, 711.2783203125, 342.5477294921875, 720.4913330078125], "page": 12, "span": [0, 51], "__ref_s3_data": null}], "text": "Table 3-2 lists the nine built-in global variables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 690.177001953125, 201.1814727783203, 698.501953125], "page": 12, "span": [0, 35], "__ref_s3_data": null}], "text": "Table 3-2 Built-in global variables", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/4"}, {"prov": [{"bbox": [64.80000305175781, 455.0362854003906, 384.3638916015625, 469.7992858886719], "page": 12, "span": [0, 34], "__ref_s3_data": null}], "text": "3.3 VERIFY_GROUP_FOR_USER function", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 356.2593994140625, 547.2347412109375, 437.4712829589844], "page": 12, "span": [0, 576], "__ref_s3_data": null}], "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80001831054688, 310.2999572753906, 547.2573852539062, 343.5125732421875], "page": 12, "span": [0, 235], "__ref_s3_data": null}], "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80001831054688, 288.2803955078125, 458.44525146484375, 297.4933776855469], "page": 12, "span": [0, 63], "__ref_s3_data": null}], "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80001831054688, 271.2408142089844, 406.0775146484375, 280.45379638671875], "page": 12, "span": [0, 57], "__ref_s3_data": null}], "text": "- 1. There are user profiles for MGR, JANE, JUDY, and TONY.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.80001831054688, 254.26100158691406, 396.9881591796875, 263.4739990234375], "page": 12, "span": [0, 58], "__ref_s3_data": null}], "text": "- 2. The user profile JANE specifies a group profile of MGR.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.80001831054688, 225.28138732910156, 536.568603515625, 246.4941864013672], "page": 12, "span": [0, 127], "__ref_s3_data": null}], "text": "- 3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.20018005371094, 150.57144165039062, 451.01605224609375, 217.305419921875], "page": 12, "span": [0, 265], "__ref_s3_data": null}], "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [136.79959106445312, 711.5667724609375, 166.73934936523438, 720.341552734375], "page": 13, "span": [0, 6], "__ref_s3_data": null}], "text": "RETURN", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 699.5669555664062, 156.7793426513672, 708.3417358398438], "page": 13, "span": [0, 4], "__ref_s3_data": null}], "text": "CASE", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 531.5695190429688, 521.5742797851562, 696.3419189453125], "page": 13, "span": [0, 437], "__ref_s3_data": null}], "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [136.79959106445312, 495.2812805175781, 547.2122192382812, 516.4940795898438], "page": 13, "span": [0, 136], "__ref_s3_data": null}], "text": "- 2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.03939819335938, 478.3014831542969, 469.1528015136719, 487.51446533203125], "page": 13, "span": [0, 62], "__ref_s3_data": null}], "text": "- -Human Resources can see the unmasked TAX_ID of the employees.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.03939819335938, 461.26190185546875, 403.95953369140625, 470.4748840332031], "page": 13, "span": [0, 50], "__ref_s3_data": null}], "text": "- -Employees can see only their own unmasked TAX_ID.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.03939819335938, 432.28228759765625, 545.16845703125, 453.4950866699219], "page": 13, "span": [0, 129], "__ref_s3_data": null}], "text": "- -Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.03939819335938, 415.302490234375, 529.463623046875, 424.5154724121094], "page": 13, "span": [0, 77], "__ref_s3_data": null}], "text": "- -Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.1997833251953, 398.2629089355469, 530.060302734375, 407.47589111328125], "page": 13, "span": [0, 82], "__ref_s3_data": null}], "text": "- To implement this column mask, run the SQL statement that is shown in Example 3-9.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 377.156982421875, 351.9873046875, 385.48199462890625], "page": 13, "span": [0, 48], "__ref_s3_data": null}], "text": "Example 3-9 Creating a mask on the TAX_ID column", "type": "paragraph", "payload": null, "name": "paragraph", "font": null}, {"prov": [{"bbox": [136.8000030517578, 107.55116271972656, 526.5546875, 368.3218994140625], "page": 13, "span": [0, 590], "__ref_s3_data": null}], "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [344.94000244140625, 28.136999130249023, 523.6016235351562, 36.461997985839844], "page": 13, "span": [0, 40], "__ref_s3_data": null}], "text": "Chapter 3. Row and Column Access Control", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 13, "span": [0, 2], "__ref_s3_data": null}], "text": "27", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.8000030517578, 711.2783203125, 449.952392578125, 720.4913330078125], "page": 14, "span": [0, 65], "__ref_s3_data": null}], "text": "- 3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.80000305175781, 610.1370239257812, 293.1380920410156, 618.4619750976562], "page": 14, "span": [0, 52], "__ref_s3_data": null}], "text": "Figure 3-10 Column masks shown in System i Navigator", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/11"}, {"prov": [{"bbox": [64.80000305175781, 577.6372680664062, 203.98521423339844, 589.6253051757812], "page": 14, "span": [0, 21], "__ref_s3_data": null}], "text": "3.6.6 Activating RCAC", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 530.2586669921875, 547.2256469726562, 563.4713134765625], "page": 14, "span": [0, 265], "__ref_s3_data": null}], "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 513.2788696289062, 409.4788818359375, 522.4918823242188], "page": 14, "span": [0, 57], "__ref_s3_data": null}], "text": "- 1. Run the SQL statements that are shown in Example 3-10.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 492.11700439453125, 375.2909851074219, 500.4420166015625], "page": 14, "span": [0, 51], "__ref_s3_data": null}], "text": "Example 3-10 Activating RCAC on the EMPLOYEES table", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 474.5671081542969, 376.6766052246094, 483.3418884277344], "page": 14, "span": [0, 45], "__ref_s3_data": null}], "text": "- /* Active Row Access Control (permissions) */", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 462.5672912597656, 354.86962890625, 471.3420715332031], "page": 14, "span": [0, 39], "__ref_s3_data": null}], "text": "- /* Active Column Access Control (masks)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [365.77313232421875, 462.5672912597656, 376.6766052246094, 471.3420715332031], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "*/", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 450.5674743652344, 291.7178039550781, 459.3422546386719], "page": 14, "span": [0, 31], "__ref_s3_data": null}], "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 438.5676574707031, 271.6783142089844, 447.3424377441406], "page": 14, "span": [0, 27], "__ref_s3_data": null}], "text": "ACTIVATE ROW ACCESS CONTROL", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 426.5678405761719, 291.7178039550781, 435.3426208496094], "page": 14, "span": [0, 31], "__ref_s3_data": null}], "text": "ACTIVATE COLUMN ACCESS CONTROL;", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 378.27978515625, 540.8014526367188, 411.4924011230469], "page": 14, "span": [0, 231], "__ref_s3_data": null}], "text": "- 2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.80000305175781, 134.63710021972656, 347.4305419921875, 142.9621124267578], "page": 14, "span": [0, 65], "__ref_s3_data": null}], "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/12"}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 78.4020004272461, 37.15127944946289], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "28", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [93.42030334472656, 28.136999130249023, 334.4214172363281, 36.461997985839844], "page": 14, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.79959106445312, 687.2783203125, 514.048583984375, 720.490966796875], "page": 15, "span": [0, 228], "__ref_s3_data": null}], "text": "- 2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 303.11700439453125, 327.0932922363281, 311.4420166015625], "page": 15, "span": [0, 44], "__ref_s3_data": null}], "text": "Figure 4-68 Visual Explain with RCAC enabled", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/13"}, {"prov": [{"bbox": [136.8000030517578, 252.21875, 547.2394409179688, 285.4313659667969], "page": 15, "span": [0, 232], "__ref_s3_data": null}], "text": "- 3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.80000305175781, 116.15709686279297, 227.1014862060547, 124.48210144042969], "page": 15, "span": [0, 37], "__ref_s3_data": null}], "text": "Figure 4-69 Index advice with no RCAC", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/14"}, {"prov": [{"bbox": [214.8000030517578, 28.136999130249023, 523.5935668945312, 36.461997985839844], "page": 15, "span": [0, 70], "__ref_s3_data": null}], "text": "Chapter 4. Implementing Row and Column Access Control: Banking example", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 15, "span": [0, 2], "__ref_s3_data": null}], "text": "77", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80030822753906, 85.39237976074219, 500.697265625, 720.3270263671875], "page": 16, "span": [0, 1998], "__ref_s3_data": null}], "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 83.98200225830078, 37.15127944946289], "page": 16, "span": [0, 3], "__ref_s3_data": null}], "text": "124", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [98.94000244140625, 28.136999130249023, 339.819580078125, 36.461997985839844], "page": 16, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [287.2200012207031, 741.251953125, 414.24481201171875, 763.4519653320312], "page": 18, "span": [0, 10], "__ref_s3_data": null}], "text": "Back cover", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [27.0, 651.5399780273438, 447.3600158691406, 718.3619995117188], "page": 18, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [26.700000762939453, 525.1680297851562, 127.443603515625, 549.8280029296875], "page": 18, "span": [0, 40], "__ref_s3_data": null}], "text": "Implement roles and separation of duties", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [26.700000762939453, 469.1280212402344, 120.283203125, 507.8280334472656], "page": 18, "span": [0, 40], "__ref_s3_data": null}], "text": "Leverage row permissions on the database", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [26.700000762939453, 413.14801025390625, 121.44960021972656, 451.8480224609375], "page": 18, "span": [0, 40], "__ref_s3_data": null}], "text": "Protect columns by defining column masks", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [152.94000244140625, 468.4081115722656, 414.084228515625, 549.2714233398438], "page": 18, "span": [0, 464], "__ref_s3_data": null}], "text": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [152.9400177001953, 403.4290466308594, 414.173828125, 460.292724609375], "page": 18, "span": [0, 309], "__ref_s3_data": null}], "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [171.0, 152.3369903564453, 231.8876953125, 160.66200256347656], "page": 18, "span": [0, 12], "__ref_s3_data": null}], "text": "REDP-5110-00", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/15"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/16"}, {"prov": [{"bbox": [467.3399963378906, 489.8393859863281, 559.809326171875, 544.2816772460938], "page": 18, "span": [0, 44], "__ref_s3_data": null}], "text": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.3399963378906, 405.52801513671875, 587.38916015625, 440.2080078125], "page": 18, "span": [0, 60], "__ref_s3_data": null}], "text": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.3399963378906, 250.36593627929688, 587.5205078125, 392.13970947265625], "page": 18, "span": [0, 323], "__ref_s3_data": null}], "text": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.3399963378906, 190.48809814453125, 570.947998046875, 213.1680908203125], "page": 18, "span": [0, 39], "__ref_s3_data": null}], "text": "For more information: ibm.com /redbooks", "type": "paragraph", "payload": null, "name": "Text", "font": null}], "figures": [{"prov": [{"bbox": [513.4560546875, 737.1808471679688, 586.1583251953125, 765.9149169921875], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [33.090599060058594, 89.5469970703125, 585.1502075195312, 498.9671630859375], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [316.94049072265625, 17.57415771484375, 581.3547973632812, 81.8721923828125], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [143.39866638183594, 506.378662109375, 179.56256103515625, 521.7388916015625], "page": 3, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [64.16704559326172, 103.87176513671875, 258.77435302734375, 188.49365234375], "page": 3, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [142.52883911132812, 288.79351806640625, 251.47850036621094, 416.9550476074219], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [145.41445922851562, 156.616943359375, 252.08840942382812, 264.7552490234375], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [32.05510711669922, 553.9590454101562, 239.62696838378906, 721.5736694335938], "page": 5, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.92466735839844, 103.39019775390625, 546.4456176757812, 416.0727844238281], "page": 7, "span": [0, 43], "__ref_s3_data": null}], "text": "Figure 1-2 Existing row and column controls", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.9717559814453, 381.39068603515625, 545.4180297851562, 684.5892333984375], "page": 10, "span": [0, 177], "__ref_s3_data": null}], "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.Figure 3-1 CREATE PERMISSION SQL statement", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.64837646484375, 197.24334716796875, 301.2367248535156, 407.8263244628906], "page": 11, "span": [0, 50], "__ref_s3_data": null}], "text": "Figure 3-5 Special registers and adopted authority", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [63.80192184448242, 621.9678955078125, 547.11474609375, 696.6176147460938], "page": 14, "span": [0, 52], "__ref_s3_data": null}], "text": "Figure 3-10 Column masks shown in System i Navigator", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [63.985130310058594, 145.86041259765625, 530.0478515625, 364.0950012207031], "page": 14, "span": [0, 65], "__ref_s3_data": null}], "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [136.5016632080078, 314.45880126953125, 545.4508666992188, 672.7509155273438], "page": 15, "span": [0, 44], "__ref_s3_data": null}], "text": "Figure 4-68 Visual Explain with RCAC enabled", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [64.27847290039062, 127.91290283203125, 506.39263916015625, 238.41851806640625], "page": 15, "span": [0, 37], "__ref_s3_data": null}], "text": "Figure 4-69 Index advice with no RCAC", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [485.1698303222656, 737.8084106445312, 566.2962036132812, 766.7407836914062], "page": 18, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [474.35540771484375, 602.1873779296875, 592.2726440429688, 711.9486694335938], "page": 18, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [136.79701232910156, 76.6675033569336, 549.8472290039062, 659.3513793945312], "page": 2, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table-of-contents", "payload": null, "#-cols": 2, "#-rows": 43, "data": [[{"bbox": [136.8000030517578, 650.1383666992188, 172.89404296875, 659.3513793945312], "spans": [[0, 0]], "text": "Notices", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [175.01951599121094, 650.1383666992188, 547.1898193359375, 659.3513793945312], "spans": [[0, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [136.79901123046875, 637.6585083007812, 189.86537170410156, 646.8715209960938], "spans": [[1, 0]], "text": "Trademarks", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [195.3968505859375, 637.6585083007812, 547.182861328125, 646.8715209960938], "spans": [[1, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [136.79901123046875, 615.1588745117188, 279.3973083496094, 624.3718872070312], "spans": [[2, 0]], "text": "DB2 for i Center of Excellence", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [280.6194152832031, 615.1588745117188, 547.1907958984375, 624.3718872070312], "spans": [[2, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [136.79901123046875, 592.6592407226562, 172.84423828125, 601.8722534179688], "spans": [[3, 0]], "text": "Preface", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [175.01852416992188, 592.6592407226562, 547.182861328125, 601.8722534179688], "spans": [[3, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [136.79803466796875, 580.1793823242188, 547.1808471679688, 589.3923950195312], "spans": [[4, 0]], "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": null, "spans": [[4, 1]], "text": "", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}], [{"bbox": [136.79803466796875, 567.6397705078125, 339.18292236328125, 576.852783203125], "spans": [[5, 0]], "text": "Now you can become a published author, too!", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [344.714111328125, 567.6397705078125, 547.1387939453125, 576.852783203125], "spans": [[5, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}], [{"bbox": [136.79803466796875, 555.159912109375, 529.9950561523438, 564.3729248046875], "spans": [[6, 0]], "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [535.5494995117188, 555.159912109375, 547.1978759765625, 564.3729248046875], "spans": [[6, 1]], "text": "xiii", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 6, "row-header": false, "row-span": [6, 7]}], [{"bbox": [136.79806518554688, 542.6800537109375, 284.0286560058594, 551.89306640625], "spans": [[7, 0]], "text": "Stay connected to IBM Redbooks", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": [289.54449462890625, 542.6800537109375, 547.1211547851562, 551.89306640625], "spans": [[7, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 7, "row-header": false, "row-span": [7, 8]}], [{"bbox": [136.79806518554688, 520.180419921875, 536.0958862304688, 529.3934326171875], "spans": [[8, 0]], "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": [541.6468505859375, 520.180419921875, 547.1978149414062, 529.3934326171875], "spans": [[8, 1]], "text": "1", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 8, "row-header": false, "row-span": [8, 9]}], [{"bbox": [136.79808044433594, 508.18060302734375, 549.8472290039062, 517.3936157226562], "spans": [[9, 0]], "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 1]], "text": "", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 9, "row-header": false, "row-span": [9, 10]}], [{"bbox": [136.79806518554688, 495.6409606933594, 536.1293334960938, 504.85394287109375], "spans": [[10, 0]], "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": [541.6611328125, 495.6409606933594, 547.19287109375, 504.85394287109375], "spans": [[10, 1]], "text": "2", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 10, "row-header": false, "row-span": [10, 11]}], [{"bbox": [136.79806518554688, 483.16107177734375, 549.8472290039062, 492.3740539550781], "spans": [[11, 0]], "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 1]], "text": "", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 11, "row-header": false, "row-span": [11, 12]}], [{"bbox": [151.19720458984375, 470.6811828613281, 536.0551147460938, 479.8941650390625], "spans": [[12, 0]], "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": [541.6015014648438, 470.6811828613281, 547.14794921875, 479.8941650390625], "spans": [[12, 1]], "text": "4", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 12, "row-header": false, "row-span": [12, 13]}], [{"bbox": [151.19720458984375, 458.14154052734375, 536.080078125, 467.3545227050781], "spans": [[13, 0]], "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 13, "row-header": false, "row-span": [13, 14]}, {"bbox": [541.635498046875, 458.14154052734375, 547.19091796875, 467.3545227050781], "spans": [[13, 1]], "text": "5", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 13, "row-header": false, "row-span": [13, 14]}], [{"bbox": [136.7970428466797, 435.64190673828125, 536.0908813476562, 444.8548889160156], "spans": [[14, 0]], "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 14, "row-header": false, "row-span": [14, 15]}, {"bbox": [541.642822265625, 435.64190673828125, 547.1947631835938, 444.8548889160156], "spans": [[14, 1]], "text": "7", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 14, "row-header": false, "row-span": [14, 15]}], [{"bbox": [136.7970428466797, 423.64208984375, 536.1271362304688, 432.8550720214844], "spans": [[15, 0]], "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 15, "row-header": false, "row-span": [15, 16]}, {"bbox": [541.6658935546875, 423.64208984375, 547.2047119140625, 432.8550720214844], "spans": [[15, 1]], "text": "8", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 15, "row-header": false, "row-span": [15, 16]}], [{"bbox": [151.19720458984375, 411.1622009277344, 535.9526977539062, 420.37518310546875], "spans": [[16, 0]], "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 16, "row-header": false, "row-span": [16, 17]}, {"bbox": [541.5558471679688, 411.1622009277344, 547.1590576171875, 420.37518310546875], "spans": [[16, 1]], "text": "8", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 16, "row-header": false, "row-span": [16, 17]}], [{"bbox": [151.19720458984375, 398.68231201171875, 536.0410766601562, 407.8952941894531], "spans": [[17, 0]], "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 17, "row-header": false, "row-span": [17, 18]}, {"bbox": [541.595947265625, 398.68231201171875, 547.1508178710938, 407.8952941894531], "spans": [[17, 1]], "text": "8", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 17, "row-header": false, "row-span": [17, 18]}], [{"bbox": [151.19720458984375, 386.1426696777344, 536.0748901367188, 395.35565185546875], "spans": [[18, 0]], "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 18, "row-header": false, "row-span": [18, 19]}, {"bbox": [541.6302490234375, 386.1426696777344, 547.1856079101562, 395.35565185546875], "spans": [[18, 1]], "text": "9", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 18, "row-header": false, "row-span": [18, 19]}], [{"bbox": [151.19720458984375, 373.66278076171875, 411.2704772949219, 382.8757629394531], "spans": [[19, 0]], "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 19, "row-header": false, "row-span": [19, 20]}, {"bbox": [416.8177490234375, 373.66278076171875, 547.1786499023438, 382.8757629394531], "spans": [[19, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 19, "row-header": false, "row-span": [19, 20]}], [{"bbox": [151.19720458984375, 361.1828918457031, 536.035888671875, 370.3958740234375], "spans": [[20, 0]], "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 20, "row-header": false, "row-span": [20, 21]}, {"bbox": [541.5989379882812, 361.1828918457031, 547.1619262695312, 370.3958740234375], "spans": [[20, 1]], "text": "9", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 20, "row-header": false, "row-span": [20, 21]}], [{"bbox": [151.19720458984375, 348.64324951171875, 530.5731811523438, 357.8562316894531], "spans": [[21, 0]], "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 21, "row-header": false, "row-span": [21, 22]}, {"bbox": [536.1044311523438, 348.64324951171875, 547.1668701171875, 357.8562316894531], "spans": [[21, 1]], "text": "10", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 21, "row-header": false, "row-span": [21, 22]}], [{"bbox": [151.19720458984375, 336.1633605957031, 530.5352172851562, 345.3763427734375], "spans": [[22, 0]], "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 22, "row-header": false, "row-span": [22, 23]}, {"bbox": [536.0755004882812, 336.1633605957031, 547.156005859375, 345.3763427734375], "spans": [[22, 1]], "text": "10", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 22, "row-header": false, "row-span": [22, 23]}], [{"bbox": [136.7970428466797, 323.6834716796875, 547.256591796875, 332.8964538574219], "spans": [[23, 0]], "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 23, "row-header": false, "row-span": [23, 24]}, {"bbox": null, "spans": [[23, 1]], "text": "", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 23, "row-header": false, "row-span": [23, 24]}], [{"bbox": [136.79702758789062, 301.183837890625, 530.5396118164062, 310.3968200683594], "spans": [[24, 0]], "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 24, "row-header": false, "row-span": [24, 25]}, {"bbox": [536.0916748046875, 301.183837890625, 547.19580078125, 310.3968200683594], "spans": [[24, 1]], "text": "13", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 24, "row-header": false, "row-span": [24, 25]}], [{"bbox": [136.79702758789062, 289.18402099609375, 530.4808959960938, 298.3970031738281], "spans": [[25, 0]], "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 25, "row-header": false, "row-span": [25, 26]}, {"bbox": [536.04248046875, 289.18402099609375, 547.1657104492188, 298.3970031738281], "spans": [[25, 1]], "text": "14", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 25, "row-header": false, "row-span": [25, 26]}], [{"bbox": [151.1971893310547, 276.6443786621094, 378.2078552246094, 285.85736083984375], "spans": [[26, 0]], "text": "3.1.1 Row permission and column mask definitions", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 26, "row-header": false, "row-span": [26, 27]}, {"bbox": [383.74713134765625, 276.6443786621094, 547.15576171875, 285.85736083984375], "spans": [[26, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 26, "row-header": false, "row-span": [26, 27]}], [{"bbox": [151.1971893310547, 264.16448974609375, 530.4347534179688, 273.3774719238281], "spans": [[27, 0]], "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 27, "row-header": false, "row-span": [27, 28]}, {"bbox": [535.9962158203125, 264.16448974609375, 547.1190795898438, 273.3774719238281], "spans": [[27, 1]], "text": "16", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 27, "row-header": false, "row-span": [27, 28]}], [{"bbox": [136.79702758789062, 251.6248321533203, 530.528076171875, 260.83782958984375], "spans": [[28, 0]], "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 28, "row-header": false, "row-span": [28, 29]}, {"bbox": [536.0670166015625, 251.6248321533203, 547.1448364257812, 260.83782958984375], "spans": [[28, 1]], "text": "18", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 28, "row-header": false, "row-span": [28, 29]}], [{"bbox": [151.1971893310547, 239.14495849609375, 530.4978637695312, 248.3579559326172], "spans": [[29, 0]], "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 29, "row-header": false, "row-span": [29, 30]}, {"bbox": [536.0518798828125, 239.14495849609375, 547.159912109375, 248.3579559326172], "spans": [[29, 1]], "text": "18", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 29, "row-header": false, "row-span": [29, 30]}], [{"bbox": [151.1971893310547, 226.6650848388672, 530.5602416992188, 235.87808227539062], "spans": [[30, 0]], "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 30, "row-header": false, "row-span": [30, 31]}, {"bbox": [536.09912109375, 226.6650848388672, 547.1768798828125, 235.87808227539062], "spans": [[30, 1]], "text": "19", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 30, "row-header": false, "row-span": [30, 31]}], [{"bbox": [136.79702758789062, 214.1254425048828, 530.5302734375, 223.33843994140625], "spans": [[31, 0]], "text": "3.3 VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 31, "row-header": false, "row-span": [31, 32]}, {"bbox": [536.0615234375, 214.1254425048828, 547.1240234375, 223.33843994140625], "spans": [[31, 1]], "text": "20", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 31, "row-header": false, "row-span": [31, 32]}], [{"bbox": [136.79702758789062, 201.64556884765625, 530.6299438476562, 210.8585662841797], "spans": [[32, 0]], "text": "3.4 Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 32, "row-header": false, "row-span": [32, 33]}, {"bbox": [536.1631469726562, 201.64556884765625, 547.2295532226562, 210.8585662841797], "spans": [[32, 1]], "text": "21", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 32, "row-header": false, "row-span": [32, 33]}], [{"bbox": [136.79701232910156, 189.1656951904297, 394.78179931640625, 198.37869262695312], "spans": [[33, 0]], "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 33, "row-header": false, "row-span": [33, 34]}, {"bbox": [400.3206481933594, 189.1656951904297, 547.10009765625, 198.37869262695312], "spans": [[33, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . 22", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 33, "row-header": false, "row-span": [33, 34]}], [{"bbox": [136.79701232910156, 176.6260528564453, 530.5651245117188, 185.83905029296875], "spans": [[34, 0]], "text": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 34, "row-header": false, "row-span": [34, 35]}, {"bbox": [536.1119995117188, 176.6260528564453, 547.2057495117188, 185.83905029296875], "spans": [[34, 1]], "text": "22", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 34, "row-header": false, "row-span": [34, 35]}], [{"bbox": [151.19717407226562, 164.14617919921875, 530.4913940429688, 173.3591766357422], "spans": [[35, 0]], "text": "3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 35, "row-header": false, "row-span": [35, 36]}, {"bbox": [536.0463256835938, 164.14617919921875, 547.1561889648438, 173.3591766357422], "spans": [[35, 1]], "text": "23", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 35, "row-header": false, "row-span": [35, 36]}], [{"bbox": [151.19717407226562, 151.6663055419922, 530.5645751953125, 160.87930297851562], "spans": [[36, 0]], "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 36, "row-header": false, "row-span": [36, 37]}, {"bbox": [536.0960083007812, 151.6663055419922, 547.1587524414062, 160.87930297851562], "spans": [[36, 1]], "text": "23", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 36, "row-header": false, "row-span": [36, 37]}], [{"bbox": [151.19717407226562, 139.1266632080078, 530.5569458007812, 148.33966064453125], "spans": [[37, 0]], "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 37, "row-header": false, "row-span": [37, 38]}, {"bbox": [536.0881958007812, 139.1266632080078, 547.1507568359375, 148.33966064453125], "spans": [[37, 1]], "text": "24", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 37, "row-header": false, "row-span": [37, 38]}], [{"bbox": [151.19717407226562, 126.64678955078125, 530.5341186523438, 135.8597869873047], "spans": [[38, 0]], "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 38, "row-header": false, "row-span": [38, 39]}, {"bbox": [536.072998046875, 126.64678955078125, 547.15087890625, 135.8597869873047], "spans": [[38, 1]], "text": "25", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 38, "row-header": false, "row-span": [38, 39]}], [{"bbox": [151.19717407226562, 114.16690826416016, 339.4510498046875, 123.37991333007812], "spans": [[39, 0]], "text": "3.6.5 Defining and creating column masks", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 39, "row-header": false, "row-span": [39, 40]}, {"bbox": [344.9899597167969, 114.16690826416016, 547.160888671875, 123.37991333007812], "spans": [[39, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 39, "row-header": false, "row-span": [39, 40]}], [{"bbox": [151.19717407226562, 101.62727355957031, 530.541015625, 110.84027099609375], "spans": [[40, 0]], "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 40, "row-header": false, "row-span": [40, 41]}, {"bbox": [536.087646484375, 101.62727355957031, 547.1808471679688, 110.84027099609375], "spans": [[40, 1]], "text": "28", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 40, "row-header": false, "row-span": [40, 41]}], [{"bbox": [151.19717407226562, 89.14738464355469, 530.5750732421875, 98.36038970947266], "spans": [[41, 0]], "text": "3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 41, "row-header": false, "row-span": [41, 42]}, {"bbox": [536.1066284179688, 89.14738464355469, 547.169677734375, 98.36038970947266], "spans": [[41, 1]], "text": "29", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 41, "row-header": false, "row-span": [41, 42]}], [{"bbox": [151.19717407226562, 76.6675033569336, 530.436279296875, 85.88050842285156], "spans": [[42, 0]], "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 42, "row-header": false, "row-span": [42, 43]}, {"bbox": [535.9984741210938, 76.6675033569336, 547.1228637695312, 85.88050842285156], "spans": [[42, 1]], "text": "32", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 42, "row-header": false, "row-span": [42, 43]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [142.79998779296875, 356.15631103515625, 539.1071166992188, 495.4620056152344], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 2-1 FUNCTION_USAGE view", "type": "table", "payload": null, "#-cols": 3, "#-rows": 5, "data": [[{"bbox": [142.8000030517578, 487.1369934082031, 202.2449951171875, 495.4620056152344], "spans": [[0, 0]], "text": "Column name", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [216.8087921142578, 487.1369934082031, 257.210693359375, 495.4620056152344], "spans": [[0, 1]], "text": "Data type", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [289.47479248046875, 487.1369934082031, 338.8946838378906, 495.4620056152344], "spans": [[0, 2]], "text": "Description", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [142.8000030517578, 468.1172790527344, 203.2322998046875, 476.4422912597656], "spans": [[1, 0]], "text": "FUNCTION_ID", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [216.785400390625, 468.1172790527344, 276.00360107421875, 476.4422912597656], "spans": [[1, 1]], "text": "VARCHAR(30)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [289.45770263671875, 468.1172790527344, 359.85394287109375, 476.4422912597656], "spans": [[1, 2]], "text": "ID of the function.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [142.8000030517578, 449.156982421875, 198.66929626464844, 457.48199462890625], "spans": [[2, 0]], "text": "USER_NAME", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [216.74130249023438, 449.156982421875, 275.9234924316406, 457.48199462890625], "spans": [[2, 1]], "text": "VARCHAR(10)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [289.382080078125, 438.1166687011719, 515.0535888671875, 457.48199462890625], "spans": [[2, 2]], "text": "Name of the user profile that has a usage setting for this  function.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [142.79998779296875, 419.1563720703125, 173.98318481445312, 427.48138427734375], "spans": [[3, 0]], "text": "USAGE", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [216.773681640625, 419.1563720703125, 270.9797668457031, 427.48138427734375], "spans": [[3, 1]], "text": "VARCHAR(7)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [289.416259765625, 397.13604736328125, 539.1071166992188, 427.48138427734375], "spans": [[3, 2]], "text": "Usage setting: GLYPH<SM590000> ALLOWED: The user profile is allowed to use the function. GLYPH<SM590000> DENIED: The user profile is not allowed to use the function.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [142.8000030517578, 378.1163330078125, 196.2248992919922, 386.44134521484375], "spans": [[4, 0]], "text": "USER_TYPE", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [216.75210571289062, 378.1163330078125, 270.99871826171875, 386.44134521484375], "spans": [[4, 1]], "text": "VARCHAR(5)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [289.4316101074219, 356.15631103515625, 448.11962890625, 386.44134521484375], "spans": [[4, 2]], "text": "Type of user profile: GLYPH<SM590000> USER: The user profile is a user. GLYPH<SM590000> GROUP: The user profile is a group.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 4, "row-header": false, "row-span": [4, 5]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [70.80000305175781, 76.13793182373047, 536.7633666992188, 391.4817199707031], "page": 9, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "type": "table", "payload": null, "#-cols": 5, "#-rows": 13, "data": [[{"bbox": [70.80030059814453, 383.1567077636719, 119.78550720214844, 391.4817199707031], "spans": [[0, 0]], "text": "User action", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [424.93804931640625, 304.9800109863281, 433.2629699707031, 344.4774475097656], "spans": [[0, 1]], "text": "*JOBCTL", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [450.1380615234375, 304.9800109863281, 458.4629821777344, 390.3999328613281], "spans": [[0, 2]], "text": "QIBM_DB_SECADM", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [475.9383544921875, 304.9800109863281, 484.2632751464844, 390.465576171875], "spans": [[0, 3]], "text": "QIBM_DB_SQLADM", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [501.13836669921875, 304.9799499511719, 534.7235717773438, 390.385498046875], "spans": [[0, 4]], "text": "QIBM_DB_SYSMON No Authority", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [70.80000305175781, 285.11700439453125, 220.1568145751953, 293.4420166015625], "spans": [[1, 0]], "text": "SET CURRENT DEGREE  (SQL statement)", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": true, "row-span": [1, 2]}, {"bbox": [429.0, 285.11700439453125, 435.00299072265625, 293.4420166015625], "spans": [[1, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": null, "spans": [[1, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [480.00030517578125, 285.11700439453125, 486.0032958984375, 293.4420166015625], "spans": [[1, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": null, "spans": [[1, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [70.80001831054688, 266.1567077636719, 264.5538024902344, 274.4817199707031], "spans": [[2, 0]], "text": "CHGQRYA  command targeting a different user's job", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": true, "row-span": [2, 3]}, {"bbox": [429.0000305175781, 266.1567077636719, 435.0030212402344, 274.4817199707031], "spans": [[2, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": null, "spans": [[2, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [480.0003356933594, 266.1567077636719, 486.0033264160156, 274.4817199707031], "spans": [[2, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": null, "spans": [[2, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [70.800048828125, 247.1370086669922, 322.5057373046875, 255.46202087402344], "spans": [[3, 0]], "text": "STRDBMON  or  ENDDBMON  commands targeting a different user's job", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": true, "row-span": [3, 4]}, {"bbox": [429.0000305175781, 247.1370086669922, 435.0030212402344, 255.46202087402344], "spans": [[3, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": null, "spans": [[3, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [480.0003356933594, 247.1370086669922, 486.0033264160156, 255.46202087402344], "spans": [[3, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": null, "spans": [[3, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [70.800048828125, 228.1173095703125, 381.0218505859375, 236.44232177734375], "spans": [[4, 0]], "text": "STRDBMON  or  ENDDBMON  commands targeting a job that matches the current user", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": true, "row-span": [4, 5]}, {"bbox": [429.0000305175781, 228.1173095703125, 435.0030212402344, 236.44232177734375], "spans": [[4, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": null, "spans": [[4, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [480.0003356933594, 228.1173095703125, 486.0033264160156, 236.44232177734375], "spans": [[4, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [505.2606201171875, 228.1173095703125, 536.7633056640625, 236.44232177734375], "spans": [[4, 4]], "text": "X X", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 4, "row-header": false, "row-span": [4, 5]}], [{"bbox": [70.800048828125, 209.15701293945312, 359.5173645019531, 217.48202514648438], "spans": [[5, 0]], "text": "QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": true, "row-span": [5, 6]}, {"bbox": [429.00006103515625, 209.15701293945312, 435.0030517578125, 217.48202514648438], "spans": [[5, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": null, "spans": [[5, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [480.0003662109375, 209.15701293945312, 486.00335693359375, 217.48202514648438], "spans": [[5, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [505.26068115234375, 209.15701293945312, 511.263671875, 217.48202514648438], "spans": [[5, 4]], "text": "X", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 5, "row-header": false, "row-span": [5, 6]}], [{"bbox": [70.80007934570312, 190.13731384277344, 220.7517852783203, 198.4623260498047], "spans": [[6, 0]], "text": "Visual Explain within Run SQL scripts", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 6, "row-header": true, "row-span": [6, 7]}, {"bbox": [429.00006103515625, 190.13731384277344, 435.0030517578125, 198.4623260498047], "spans": [[6, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": null, "spans": [[6, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [480.0003662109375, 190.13731384277344, 486.00335693359375, 198.4623260498047], "spans": [[6, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [505.26068115234375, 190.13731384277344, 536.7633666992188, 198.4623260498047], "spans": [[6, 4]], "text": "X X", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 6, "row-header": false, "row-span": [6, 7]}], [{"bbox": [70.80007934570312, 171.11761474609375, 236.65480041503906, 179.442626953125], "spans": [[7, 0]], "text": "Visual Explain outside of Run SQL scripts", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 7, "row-header": true, "row-span": [7, 8]}, {"bbox": [429.00006103515625, 171.11761474609375, 435.0030517578125, 179.442626953125], "spans": [[7, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": null, "spans": [[7, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": [480.0003662109375, 171.11761474609375, 486.00335693359375, 179.442626953125], "spans": [[7, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": null, "spans": [[7, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 7, "row-header": false, "row-span": [7, 8]}], [{"bbox": [70.80007934570312, 152.15731811523438, 213.1296844482422, 160.48233032226562], "spans": [[8, 0]], "text": "ANALYZE PLAN CACHE procedure", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 8, "row-header": true, "row-span": [8, 9]}, {"bbox": [429.00006103515625, 152.15731811523438, 435.0030517578125, 160.48233032226562], "spans": [[8, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": null, "spans": [[8, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": [480.0003662109375, 152.15731811523438, 486.00335693359375, 160.48233032226562], "spans": [[8, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": null, "spans": [[8, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 8, "row-header": false, "row-span": [8, 9]}], [{"bbox": [70.80007934570312, 133.1376190185547, 199.87808227539062, 141.46263122558594], "spans": [[9, 0]], "text": "DUMP PLAN CACHE procedure", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 9, "row-header": true, "row-span": [9, 10]}, {"bbox": [429.00006103515625, 133.1376190185547, 435.0030517578125, 141.46263122558594], "spans": [[9, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": [480.0003662109375, 133.1376190185547, 486.00335693359375, 141.46263122558594], "spans": [[9, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 9, "row-header": false, "row-span": [9, 10]}], [{"bbox": [70.80007934570312, 114.11792755126953, 208.36776733398438, 122.44291687011719], "spans": [[10, 0]], "text": "MODIFY PLAN CACHE procedure", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 10, "row-header": true, "row-span": [10, 11]}, {"bbox": [429.00006103515625, 114.11792755126953, 435.0030517578125, 122.44291687011719], "spans": [[10, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": null, "spans": [[10, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": [480.0003662109375, 114.11792755126953, 486.00335693359375, 122.44291687011719], "spans": [[10, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": null, "spans": [[10, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 10, "row-header": false, "row-span": [10, 11]}], [{"bbox": [70.80007934570312, 95.09822845458984, 411.20263671875, 103.42323303222656], "spans": [[11, 0]], "text": "MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority)", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 11, "row-header": true, "row-span": [11, 12]}, {"bbox": [429.00006103515625, 95.09822845458984, 435.0030517578125, 103.42323303222656], "spans": [[11, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": [480.0003662109375, 95.09822845458984, 486.00335693359375, 103.42323303222656], "spans": [[11, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 11, "row-header": false, "row-span": [11, 12]}], [{"bbox": [70.80007934570312, 76.13793182373047, 377.1258544921875, 84.46292877197266], "spans": [[12, 0]], "text": "CHANGE PLAN CACHE SIZE procedure (currently does not check authority)", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 12, "row-header": true, "row-span": [12, 13]}, {"bbox": [429.00006103515625, 76.13793182373047, 435.0030517578125, 84.46292877197266], "spans": [[12, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": null, "spans": [[12, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": [480.0003662109375, 76.13793182373047, 486.00335693359375, 84.46292877197266], "spans": [[12, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": null, "spans": [[12, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 12, "row-header": false, "row-span": [12, 13]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [142.8000030517578, 594.1170043945312, 535.6508178710938, 681.4619750976562], "page": 11, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 3-1 Special registers and their corresponding values", "type": "table", "payload": null, "#-cols": 2, "#-rows": 4, "data": [[{"bbox": [142.8000030517578, 673.1370239257812, 209.67091369628906, 681.4619750976562], "spans": [[0, 0]], "text": "Special register", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [230.18911743164062, 673.1370239257812, 319.9352722167969, 681.4619750976562], "spans": [[0, 1]], "text": "Corresponding value", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [142.80001831054688, 643.1364135742188, 212.7012176513672, 662.5016479492188], "spans": [[1, 0]], "text": "USER or SESSION_USER", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [230.2197265625, 654.1766967773438, 467.9906921386719, 662.5016479492188], "spans": [[1, 1]], "text": "The effective user of the thread excluding adopted authority.", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [142.80003356933594, 624.11669921875, 216.63963317871094, 632.441650390625], "spans": [[2, 0]], "text": "CURRENT_USER", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [230.19813537597656, 613.13671875, 535.6508178710938, 632.441650390625], "spans": [[2, 1]], "text": "The effective user of the thread including adopted authority. When no adopted  authority is present, this has the same value as USER.", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [142.8009033203125, 594.1170043945312, 209.73570251464844, 602.4419555664062], "spans": [[3, 0]], "text": "SYSTEM_USER", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [230.24490356445312, 594.1170043945312, 425.64569091796875, 602.4419555664062], "spans": [[3, 1]], "text": "The authorization ID that initiated the connection.", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [70.80000305175781, 502.1377258300781, 527.5922241210938, 681.4619750976562], "page": 12, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 3-2 Built-in global variables", "type": "table", "payload": null, "#-cols": 3, "#-rows": 10, "data": [[{"bbox": [70.80000305175781, 673.1370239257812, 134.99070739746094, 681.4619750976562], "spans": [[0, 0]], "text": "Global variable", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [202.889404296875, 673.1370239257812, 223.34640502929688, 681.4619750976562], "spans": [[0, 1]], "text": "Type", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [281.8247985839844, 673.1370239257812, 331.3428039550781, 681.4619750976562], "spans": [[0, 2]], "text": "Description", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [70.80000305175781, 654.1766967773438, 132.7209014892578, 662.5016479492188], "spans": [[1, 0]], "text": "CLIENT_HOST", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [202.89028930664062, 654.1766967773438, 267.0765075683594, 662.5016479492188], "spans": [[1, 1]], "text": "VARCHAR(255)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [281.8473205566406, 654.1766967773438, 510.17547607421875, 662.5016479492188], "spans": [[1, 2]], "text": "Host name of the current client as returned by the system", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [70.80001831054688, 635.156982421875, 140.66522216796875, 643.48193359375], "spans": [[2, 0]], "text": "CLIENT_IPADDR", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [202.872314453125, 635.156982421875, 267.077392578125, 643.48193359375], "spans": [[2, 1]], "text": "VARCHAR(128)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [281.8454895019531, 635.156982421875, 509.6058349609375, 643.48193359375], "spans": [[2, 2]], "text": "IP address of the current client as returned by the system", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [70.80001831054688, 616.1372680664062, 134.98263549804688, 624.4622192382812], "spans": [[3, 0]], "text": "CLIENT_PORT", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [202.90293884277344, 616.1372680664062, 242.80084228515625, 624.4622192382812], "spans": [[3, 1]], "text": "INTEGER", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [281.7978515625, 616.1372680664062, 527.5922241210938, 624.4622192382812], "spans": [[3, 2]], "text": "Port 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578.1572265625, 156.01654052734375, 586.482177734375], "spans": [[5, 0]], "text": "PACKAGE_SCHEMA", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [202.83544921875, 578.1572265625, 267.0864562988281, 586.482177734375], "spans": [[5, 1]], "text": "VARCHAR(128)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [281.8707580566406, 578.1572265625, 470.44677734375, 586.482177734375], "spans": [[5, 2]], "text": "Schema name of the currently running package", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 5, "row-header": false, "row-span": [5, 6]}], [{"bbox": [70.80001831054688, 559.1375122070312, 157.89932250976562, 567.4624633789062], "spans": [[6, 0]], "text": "PACKAGE_VERSION", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [202.72471618652344, 559.1375122070312, 261.9825439453125, 567.4624633789062], "spans": [[6, 1]], "text": "VARCHAR(64)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [281.7492370605469, 559.1375122070312, 478.84381103515625, 567.4624633789062], "spans": [[6, 2]], "text": "Version identifier of the currently running package", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 6, "row-header": false, "row-span": [6, 7]}], [{"bbox": [70.80001831054688, 540.1177978515625, 154.419921875, 548.4427490234375], "spans": [[7, 0]], "text": "ROUTINE_SCHEMA", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": [202.79312133789062, 540.1177978515625, 267.0927429199219, 548.4427490234375], "spans": [[7, 1]], "text": "VARCHAR(128)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": [281.87164306640625, 540.1177978515625, 464.2602233886719, 548.4427490234375], "spans": [[7, 2]], "text": "Schema name of the currently running routine", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 7, "row-header": false, "row-span": [7, 8]}], [{"bbox": [70.80001831054688, 521.157470703125, 188.43991088867188, 529.482421875], "spans": [[8, 0]], "text": "ROUTINE_SPECIFIC_NAME", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": [202.8444061279297, 521.157470703125, 267.03692626953125, 529.482421875], "spans": [[8, 1]], "text": "VARCHAR(128)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": [281.80682373046875, 521.157470703125, 430.40045166015625, 529.482421875], "spans": [[8, 2]], "text": "Name of the currently running routine", "type": "body", "col": 2, 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All rights reserved.", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [538.8599853515625, 27.93828010559082, 547.25927734375, 37.15127944946289], "page": 2, "span": [0, 3], "__ref_s3_data": null}], "text": "iii", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 706.416015625, 235.86239624023438, 717.5160522460938], "page": 3, "span": [0, 30], "__ref_s3_data": null}], "text": "DB2 for i Center of Excellence", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [94.13269805908203, 636.66357421875, 233.99972534179688, 653.5498657226562], "page": 3, "span": [0, 52], "__ref_s3_data": null}], "text": "Solution Brief IBM Systems Lab Services and Training", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [144.8892059326172, 455.1859436035156, 188.74681091308594, 464.5383605957031], "page": 3, "span": [0, 10], "__ref_s3_data": null}], "text": "Highlights", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [144.8892059326172, 433.3105773925781, 242.87388610839844, 446.7829284667969], "page": 3, "span": [0, 532], "__ref_s3_data": null}], "text": "- GLYPH<g115>GLYPH<g3> GLYPH<g40>GLYPH<g81>GLYPH<g75>GLYPH<g68>GLYPH<g81>GLYPH<g70>GLYPH<g72>GLYPH<g3> GLYPH<g87>GLYPH<g75>GLYPH<g72>GLYPH<g3> GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g73>GLYPH<g82>GLYPH<g85>GLYPH<g80>GLYPH<g68>GLYPH<g81>GLYPH<g70>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g92>GLYPH<g82>GLYPH<g88>GLYPH<g85> GLYPH<g3> GLYPH<g71>GLYPH<g68>GLYPH<g87>GLYPH<g68>GLYPH<g69>GLYPH<g68>GLYPH<g86>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g68>GLYPH<g87>GLYPH<g76>GLYPH<g82>GLYPH<g81>GLYPH<g86>", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [144.8892059326172, 402.7626953125, 259.22869873046875, 424.06781005859375], "page": 3, "span": [0, 876], "__ref_s3_data": null}], "text": "- GLYPH<g115>GLYPH<g3> GLYPH<g40>GLYPH<g68>GLYPH<g85> GLYPH<g81>GLYPH<g3> GLYPH<g74>GLYPH<g85>GLYPH<g72>GLYPH<g68>GLYPH<g87>GLYPH<g72>GLYPH<g85>GLYPH<g3> GLYPH<g85>GLYPH<g72>GLYPH<g87>GLYPH<g88>GLYPH<g85> GLYPH<g81>GLYPH<g3> GLYPH<g82>GLYPH<g81>GLYPH<g3> GLYPH<g44>GLYPH<g55>GLYPH<g3> GLYPH<g83>GLYPH<g85>GLYPH<g82>GLYPH<g77>GLYPH<g72>GLYPH<g70>GLYPH<g87>GLYPH<g86> GLYPH<g3> GLYPH<g87>GLYPH<g75>GLYPH<g85>GLYPH<g82>GLYPH<g88>GLYPH<g74>GLYPH<g75>GLYPH<g3> GLYPH<g80>GLYPH<g82>GLYPH<g71>GLYPH<g72>GLYPH<g85> GLYPH<g81>GLYPH<g76>GLYPH<g93>GLYPH<g68>GLYPH<g87>GLYPH<g76>GLYPH<g82>GLYPH<g81>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g71>GLYPH<g68>GLYPH<g87>GLYPH<g68>GLYPH<g69>GLYPH<g68>GLYPH<g86>GLYPH<g72>GLYPH<g3> GLYPH<g68>GLYPH<g81>GLYPH<g71> GLYPH<g3> GLYPH<g68>GLYPH<g83>GLYPH<g83>GLYPH<g79>GLYPH<g76>GLYPH<g70>GLYPH<g68>GLYPH<g87>GLYPH<g76>GLYPH<g82>GLYPH<g81>GLYPH<g86>", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [144.8892059326172, 380.0474548339844, 249.8356170654297, 393.5198059082031], "page": 3, "span": [0, 672], "__ref_s3_data": null}], "text": "- GLYPH<g115>GLYPH<g3> GLYPH<g53>GLYPH<g72>GLYPH<g79>GLYPH<g92>GLYPH<g3> GLYPH<g82>GLYPH<g81>GLYPH<g3> GLYPH<g44>GLYPH<g37>GLYPH<g48>GLYPH<g3> GLYPH<g72>GLYPH<g91>GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g87>GLYPH<g3> GLYPH<g70>GLYPH<g82>GLYPH<g81>GLYPH<g86>GLYPH<g88>GLYPH<g79>GLYPH<g87>GLYPH<g76>GLYPH<g81>GLYPH<g74>GLYPH<g15>GLYPH<g3> GLYPH<g86>GLYPH<g78>GLYPH<g76>GLYPH<g79>GLYPH<g79>GLYPH<g86> GLYPH<g3> GLYPH<g86>GLYPH<g75>GLYPH<g68>GLYPH<g85>GLYPH<g76>GLYPH<g81>GLYPH<g74>GLYPH<g3> GLYPH<g68>GLYPH<g81>GLYPH<g71>GLYPH<g3> GLYPH<g85>GLYPH<g72>GLYPH<g81>GLYPH<g82>GLYPH<g90>GLYPH<g81>GLYPH<g3> GLYPH<g86>GLYPH<g72>GLYPH<g85>GLYPH<g89>GLYPH<g76>GLYPH<g70>GLYPH<g72>GLYPH<g86>", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [144.8892059326172, 357.3323669433594, 234.2516326904297, 370.8047180175781], "page": 3, "span": [0, 613], "__ref_s3_data": null}], "text": "- GLYPH<g115>GLYPH<g3> GLYPH<g55> GLYPH<g68>GLYPH<g78>GLYPH<g72>GLYPH<g3> GLYPH<g68>GLYPH<g71>GLYPH<g89>GLYPH<g68>GLYPH<g81>GLYPH<g87>GLYPH<g68>GLYPH<g74>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g68>GLYPH<g70>GLYPH<g70>GLYPH<g72>GLYPH<g86>GLYPH<g86>GLYPH<g3> GLYPH<g87>GLYPH<g82>GLYPH<g3> GLYPH<g68> GLYPH<g3> GLYPH<g90>GLYPH<g82>GLYPH<g85>GLYPH<g79>GLYPH<g71>GLYPH<g90>GLYPH<g76>GLYPH<g71>GLYPH<g72>GLYPH<g3> GLYPH<g86>GLYPH<g82>GLYPH<g88>GLYPH<g85>GLYPH<g70>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g72>GLYPH<g91>GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g87>GLYPH<g76>GLYPH<g86>GLYPH<g72>", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [461.0885925292969, 646.5781860351562, 506.26177978515625, 653.5924682617188], "page": 3, "span": [0, 14], "__ref_s3_data": null}], "text": "Power Services", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 515.3794555664062, 463.8094177246094, 552.6573486328125], "page": 3, "span": [0, 30], "__ref_s3_data": null}], "text": "DB2 for i Center of Excellence", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [280.2401123046875, 504.5404052734375, 483.29571533203125, 514.4097290039062], "page": 3, "span": [0, 49], "__ref_s3_data": null}], "text": "Expert help to achieve your business requirements", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 467.1043395996094, 443.2821044921875, 476.1183776855469], "page": 3, "span": [0, 37], "__ref_s3_data": null}], "text": "We build confident, satisfied clients", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [280.2401123046875, 447.0404968261719, 488.1546630859375, 464.6240539550781], "page": 3, "span": [0, 122], "__ref_s3_data": null}], "text": "No one else has the vast consulting experiences, skills sharing and renown service offerings to do what we can do for you.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 427.2699890136719, 367.8602294921875, 434.6739807128906], "page": 3, "span": [0, 27], "__ref_s3_data": null}], "text": "Because no one else is IBM.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 366.77972412109375, 500.321044921875, 414.9019775390625], "page": 3, "span": [0, 318], "__ref_s3_data": null}], "text": "With combined experiences and direct access to development groups, we're the experts in IBM DB2\u00ae for i. The DB2 for i Center of Excellence (CoE) can help you achieve-perhaps reexamine and exceed-your business requirements and gain more confidence and satisfaction in IBM product data management products and solutions.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 345.1319274902344, 434.8320617675781, 354.1459655761719], "page": 3, "span": [0, 30], "__ref_s3_data": null}], "text": "Who we are, some of what we do", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [280.2401123046875, 335.2477722167969, 434.56317138671875, 342.6517639160156], "page": 3, "span": [0, 46], "__ref_s3_data": null}], "text": "Global CoE engagements cover topics including:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 315.4777526855469, 401.5641174316406, 322.8817443847656], "page": 3, "span": [0, 38], "__ref_s3_data": null}], "text": "- r Database performance and scalability", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 305.2950439453125, 424.9964599609375, 312.69903564453125], "page": 3, "span": [0, 44], "__ref_s3_data": null}], "text": "- r Advanced SQL knowledge and skills transfer", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 295.1124572753906, 392.158447265625, 302.5164489746094], "page": 3, "span": [0, 37], "__ref_s3_data": null}], "text": "- r Business intelligence and analytics", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 284.92974853515625, 339.94354248046875, 292.333740234375], "page": 3, "span": [0, 15], "__ref_s3_data": null}], "text": "- r DB2 Web Query", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 274.7471618652344, 504.1931457519531, 282.1511535644531], "page": 3, "span": [0, 72], "__ref_s3_data": null}], "text": "- r Query/400 modernization for better reporting and analysis capabilities", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 264.564453125, 423.002197265625, 271.96844482421875], "page": 3, "span": [0, 43], "__ref_s3_data": null}], "text": "- r Database modernization and re-engineering", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 254.38186645507812, 399.6517333984375, 261.7858581542969], "page": 3, "span": [0, 38], "__ref_s3_data": null}], "text": "- r Data-centric architecture and design", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 244.1992645263672, 466.77880859375, 251.60325622558594], "page": 3, "span": [0, 58], "__ref_s3_data": null}], "text": "- r Extremely large database and overcoming limits to growth", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 234.0165557861328, 382.2095642089844, 241.42054748535156], "page": 3, "span": [0, 30], "__ref_s3_data": null}], "text": "- r ISV education and enablement", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.80000305175781, 695.9519653320312, 151.46160888671875, 718.1519775390625], "page": 4, "span": [0, 7], "__ref_s3_data": null}], "text": "Preface", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.79983520507812, 590.1392822265625, 547.3082275390625, 659.3513793945312], "page": 4, "span": [0, 469], "__ref_s3_data": null}], "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79986572265625, 532.1800537109375, 546.4656982421875, 577.3925170898438], "page": 4, "span": [0, 309], "__ref_s3_data": null}], "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 450.1584777832031, 547.2366943359375, 471.37127685546875], "page": 4, "span": [0, 172], "__ref_s3_data": null}], "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [263.3995666503906, 275.1402587890625, 541.2507934570312, 416.3512268066406], "page": 4, "span": [0, 684], "__ref_s3_data": null}], "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/6"}, {"prov": [{"bbox": [64.80000305175781, 28.136999130249023, 257.24334716796875, 36.461997985839844], "page": 4, "span": [0, 48], "__ref_s3_data": null}], "text": "' Copyright IBM Corp. 2014. All rights reserved.", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [263.39959716796875, 111.162841796875, 541.2737426757812, 264.37347412109375], "page": 4, "span": [0, 726], "__ref_s3_data": null}], "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [538.8599853515625, 27.93828010559082, 547.2503051757812, 37.15127944946289], "page": 4, "span": [0, 2], "__ref_s3_data": null}], "text": "xi", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 488.9364013671875, 125.36660766601562, 503.69940185546875], "page": 4, "span": [0, 7], "__ref_s3_data": null}], "text": "Authors", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/7"}, {"prov": [{"bbox": [81.0, 517.019287109375, 115.13253021240234, 523.457275390625], "page": 5, "span": [0, 10], "__ref_s3_data": null}], "text": "Chapter 1.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [500.3999938964844, 661.8682861328125, 522.6177368164062, 698.831298828125], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "1", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 482.1217956542969, 547.3047485351562, 537.1136474609375], "page": 5, "span": [0, 36], "__ref_s3_data": null}], "text": "Securing and protecting IBM DB2 data", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.79965209960938, 362.078857421875, 547.2540283203125, 443.2912902832031], "page": 5, "span": [0, 648], "__ref_s3_data": null}], "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80023193359375, 304.0598449707031, 527.206298828125, 349.27227783203125], "page": 5, "span": [0, 304], "__ref_s3_data": null}], "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8002471923828, 270.1002197265625, 547.1551513671875, 291.3130187988281], "page": 5, "span": [0, 122], "__ref_s3_data": null}], "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8002471923828, 253.06063842773438, 250.23167419433594, 262.2736511230469], "page": 5, "span": [0, 37], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Security fundamentals", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8002471923828, 241.0608367919922, 282.98114013671875, 250.27383422851562], "page": 5, "span": [0, 47], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Current state of IBM i security", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8002471923828, 229.06103515625, 264.8818664550781, 238.27403259277344], "page": 5, "span": [0, 43], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> DB2 for i security controls", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 67.21955871582031, 258.362548828125, 74.24993896484375], "page": 5, "span": [0, 35], "__ref_s3_data": null}], "text": "$^{1 }$http://www.idtheftcenter.org", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [136.8000030517578, 57.02824020385742, 234.05880737304688, 64.40973663330078], "page": 5, "span": [0, 31], "__ref_s3_data": null}], "text": "$^{2 }$http://www.ponemon.org /", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [64.80000305175781, 28.136999130249023, 257.24334716796875, 36.461997985839844], "page": 5, "span": [0, 48], "__ref_s3_data": null}], "text": "' Copyright IBM Corp. 2014. All rights reserved.", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [541.6798706054688, 27.93828010559082, 547.2176513671875, 37.15127944946289], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "1", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 702.8963012695312, 267.40582275390625, 717.6593017578125], "page": 6, "span": [0, 25], "__ref_s3_data": null}], "text": "1.1 Security fundamentals", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 664.178466796875, 545.0048217773438, 685.3912963867188], "page": 6, "span": [0, 133], "__ref_s3_data": null}], "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 611.138916015625, 547.1642456054688, 656.8751220703125], "page": 6, "span": [0, 361], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.199462890625, 522.1602172851562, 547.2608642578125, 603.3721313476562], "page": 6, "span": [0, 587], "__ref_s3_data": null}], "text": "- The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.199462890625, 505.180419921875, 541.9920043945312, 514.3934326171875], "page": 6, "span": [0, 90], "__ref_s3_data": null}], "text": "A security policy is what defines whether the system and its settings are secure (or not).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79930114746094, 416.139404296875, 547.1582641601562, 497.8750305175781], "page": 6, "span": [0, 573], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8002166748047, 382.1797790527344, 535.3616943359375, 403.392578125], "page": 6, "span": [0, 179], "__ref_s3_data": null}], "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 338.936279296875, 323.3839111328125, 353.69927978515625], "page": 6, "span": [0, 35], "__ref_s3_data": null}], "text": "1.2 Current state of IBM i security", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 276.1588439941406, 547.3182373046875, 321.37127685546875], "page": 6, "span": [0, 306], "__ref_s3_data": null}], "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 206.1400604248047, 547.284423828125, 263.3522644042969], "page": 6, "span": [0, 405], "__ref_s3_data": null}], "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 112.12167358398438, 547.2832641601562, 193.33349609375], "page": 6, "span": [0, 640], "__ref_s3_data": null}], "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 72.8219985961914, 37.15127944946289], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "2", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [87.84030151367188, 28.136999130249023, 328.7253723144531, 36.461997985839844], "page": 6, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 72.8219985961914, 37.15127944946289], "page": 7, "span": [0, 1], "__ref_s3_data": null}], "text": "4", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [87.84030151367188, 28.136999130249023, 328.7253723144531, 36.461997985839844], "page": 7, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.8000030517578, 639.2794189453125, 544.3033447265625, 720.4913330078125], "page": 7, "span": [0, 589], "__ref_s3_data": null}], "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 606.67724609375, 301.4690246582031, 618.665283203125], "page": 7, "span": [0, 37], "__ref_s3_data": null}], "text": "1.3.1 Existing row and column control", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.79998779296875, 535.2990112304688, 541.5673828125, 592.5112915039062], "page": 7, "span": [0, 377], "__ref_s3_data": null}], "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79998779296875, 477.27996826171875, 547.4407958984375, 522.492431640625], "page": 7, "span": [0, 340], "__ref_s3_data": null}], "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79998779296875, 431.2607727050781, 547.232666015625, 464.473388671875], "page": 7, "span": [0, 247], "__ref_s3_data": null}], "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 91.85700225830078, 316.447265625, 100.18199920654297], "page": 7, "span": [0, 43], "__ref_s3_data": null}], "text": "Figure 1-2 Existing row and column controls", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/8"}, {"prov": [{"bbox": [64.80000305175781, 708.67724609375, 335.4955139160156, 720.665283203125], "page": 8, "span": [0, 38], "__ref_s3_data": null}], "text": "2.1.6 Change Function Usage CL command", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 685.2982788085938, 547.284423828125, 694.5112915039062], "page": 8, "span": [0, 90], "__ref_s3_data": null}], "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 668.2587280273438, 301.5174865722656, 677.4717407226562], "page": 8, "span": [0, 49], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.80099487304688, 656.2589111328125, 313.39776611328125, 665.471923828125], "page": 8, "span": [0, 51], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8009796142578, 644.2590942382812, 310.8171081542969, 653.4721069335938], "page": 8, "span": [0, 52], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.7999725341797, 610.2994995117188, 512.5380249023438, 631.5123291015625], "page": 8, "span": [0, 126], "__ref_s3_data": null}], "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80096435546875, 593.5487670898438, 441.59686279296875, 602.3235473632812], "page": 8, "span": [0, 61], "__ref_s3_data": null}], "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 560.6572875976562, 544.4754638671875, 572.6453247070312], "page": 8, "span": [0, 72], "__ref_s3_data": null}], "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 525.2785034179688, 519.5179443359375, 546.4913330078125], "page": 8, "span": [0, 130], "__ref_s3_data": null}], "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 504.11700439453125, 283.9680480957031, 512.4420166015625], "page": 8, "span": [0, 29], "__ref_s3_data": null}], "text": "Table 2-1 FUNCTION_USAGE view", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [136.8000030517578, 318.2784729003906, 547.2803955078125, 339.49127197265625], "page": 8, "span": [0, 112], "__ref_s3_data": null}], "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 297.11700439453125, 462.35418701171875, 305.4420166015625], "page": 8, "span": [0, 74], "__ref_s3_data": null}], "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "type": "paragraph", "payload": null, "name": "paragraph", "font": null}, {"prov": [{"bbox": [136.8, 279.56719999999996, 171.26956, 288.34198], "page": 8, "span": [0, 6], "__ref_s3_data": null}], "text": "SELECT", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [182.75941, 279.56719999999996, 251.69853, 288.34198], "page": 8, "span": [0, 12], "__ref_s3_data": null}], "text": "function_id,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [166.78244, 267.56737999999996, 241.73852999999997, 276.3421599999999], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "user_name,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [170.75961, 255.56758000000002, 221.69901999999996, 264.34235], "page": 8, "span": [0, 6], "__ref_s3_data": null}], "text": "usage,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [167.53809, 243.56777999999997, 236.69878, 252.34253], "page": 8, "span": [0, 9], "__ref_s3_data": null}], "text": "user_type", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 231.56798000000003, 160.59396, 240.34272999999996], "page": 8, "span": [0, 4], "__ref_s3_data": null}], "text": "FROM", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [178.43944, 231.56798000000003, 261.71829, 240.34272999999996], "page": 8, "span": [0, 14], "__ref_s3_data": null}], "text": "function_usage", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 219.56817999999998, 162.44176, 228.34293000000002], "page": 8, "span": [0, 5], "__ref_s3_data": null}], "text": "WHERE", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [177.8268, 219.56817999999998, 331.67731, 228.34293000000002], "page": 8, "span": [0, 28], "__ref_s3_data": null}], "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 207.56836999999996, 178.77542, 216.34312], "page": 8, "span": [0, 8], "__ref_s3_data": null}], "text": "ORDER BY", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [189.26929, 207.56836999999996, 241.73856, 216.34312], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "user_name;", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 157.01637268066406, 249.59605407714844, 171.7793731689453], "page": 8, "span": [0, 24], "__ref_s3_data": null}], "text": "2.2 Separation of duties", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 82.23904418945312, 547.2234497070312, 139.45127868652344], "page": 8, "span": [0, 463], "__ref_s3_data": null}], "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 78.4020004272461, 37.15127944946289], "page": 8, "span": [0, 2], "__ref_s3_data": null}], "text": "10", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [93.42030334472656, 28.136999130249023, 334.4214172363281, 36.461997985839844], "page": 8, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.79959106445312, 651.2788696289062, 542.6943359375, 720.490966796875], "page": 9, "span": [0, 516], "__ref_s3_data": null}], "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 593.2598266601562, 547.303955078125, 638.4722900390625], "page": 9, "span": [0, 285], "__ref_s3_data": null}], "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 559.3002319335938, 538.6507568359375, 580.5130615234375], "page": 9, "span": [0, 129], "__ref_s3_data": null}], "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 513.281005859375, 545.7960205078125, 546.49365234375], "page": 9, "span": [0, 204], "__ref_s3_data": null}], "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 455.2619934082031, 539.80712890625, 500.47442626953125], "page": 9, "span": [0, 285], "__ref_s3_data": null}], "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 421.3023681640625, 543.067138671875, 442.5151672363281], "page": 9, "span": [0, 136], "__ref_s3_data": null}], "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 400.1369934082031, 391.754638671875, 408.4620056152344], "page": 9, "span": [0, 78], "__ref_s3_data": null}], "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"prov": [{"bbox": [355.32000732421875, 28.136999130249023, 523.5407104492188, 36.461997985839844], "page": 9, "span": [0, 41], "__ref_s3_data": null}], "text": "Chapter 2. Roles and separation of duties", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 9, "span": [0, 2], "__ref_s3_data": null}], "text": "11", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.799560546875, 699.2781372070312, 528.7305908203125, 720.490966796875], "page": 10, "span": [0, 135], "__ref_s3_data": null}], "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [136.8000030517578, 369.5369873046875, 341.9765930175781, 377.86199951171875], "page": 10, "span": [0, 42], "__ref_s3_data": null}], "text": "Figure 3-1 CREATE PERMISSION SQL statement", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/9"}, {"prov": [{"bbox": [136.8000030517578, 340.95599365234375, 215.37600708007812, 352.0559997558594], "page": 10, "span": [0, 11], "__ref_s3_data": null}], "text": "Column mask", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 291.6988525390625, 542.7664794921875, 336.9112854003906], "page": 10, "span": [0, 297], "__ref_s3_data": null}], "text": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [344.94000244140625, 28.136999130249023, 523.6016235351562, 36.461997985839844], "page": 10, "span": [0, 40], "__ref_s3_data": null}], "text": "Chapter 3. Row and Column Access Control", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 10, "span": [0, 2], "__ref_s3_data": null}], "text": "15", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.79959106445312, 711.2779541015625, 412.20758056640625, 720.490966796875], "page": 11, "span": [0, 62], "__ref_s3_data": null}], "text": "Table 3-1 summarizes these special registers and their values.", "type": "paragraph", "payload": null, "name": "paragraph", "font": null}, {"prov": [{"bbox": [136.8000030517578, 690.177001953125, 372.6036376953125, 698.501953125], "page": 11, "span": [0, 58], "__ref_s3_data": null}], "text": "Table 3-1 Special registers and their corresponding values", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/3"}, {"prov": [{"bbox": [136.8000030517578, 556.2984619140625, 538.493896484375, 577.5112915039062], "page": 11, "span": [0, 97], "__ref_s3_data": null}], "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 539.2589111328125, 411.36138916015625, 548.471923828125], "page": 11, "span": [0, 75], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> A user connects to the server using the user profile ALICE.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 522.2791137695312, 453.2580871582031, 531.4921264648438], "page": 11, "span": [0, 77], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 493.29949951171875, 541.4498291015625, 514.5123291015625], "page": 11, "span": [0, 160], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 452.2602844238281, 547.2167358398438, 485.472900390625], "page": 11, "span": [0, 253], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.80101013183594, 423.2806701660156, 547.3540649414062, 444.49346923828125], "page": 11, "span": [0, 133], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 186.95709228515625, 341.2566223144531, 195.2821044921875], "page": 11, "span": [0, 50], "__ref_s3_data": null}], "text": "Figure 3-5 Special registers and adopted authority", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/10"}, {"prov": [{"bbox": [64.80000305175781, 154.457275390625, 247.02536010742188, 166.44528198242188], "page": 11, "span": [0, 31], "__ref_s3_data": null}], "text": "3.2.2 Built-in global variables", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 119.0784683227539, 518.0011596679688, 140.29127502441406], "page": 11, "span": [0, 161], "__ref_s3_data": null}], "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 73.05928039550781, 532.3385009765625, 106.27189636230469], "page": 11, "span": [0, 233], "__ref_s3_data": null}], "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [344.94000244140625, 28.136999130249023, 523.6016235351562, 36.461997985839844], "page": 11, "span": [0, 40], "__ref_s3_data": null}], "text": "Chapter 3. Row and Column Access Control", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 11, "span": [0, 2], "__ref_s3_data": null}], "text": "19", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 78.4020004272461, 37.15127944946289], "page": 12, "span": [0, 2], "__ref_s3_data": null}], "text": "20", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [93.42030334472656, 28.136999130249023, 334.4214172363281, 36.461997985839844], "page": 12, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.8000030517578, 711.2783203125, 342.5477294921875, 720.4913330078125], "page": 12, "span": [0, 51], "__ref_s3_data": null}], "text": "Table 3-2 lists the nine built-in global variables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 690.177001953125, 201.1814727783203, 698.501953125], "page": 12, "span": [0, 35], "__ref_s3_data": null}], "text": "Table 3-2 Built-in global variables", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/4"}, {"prov": [{"bbox": [64.80000305175781, 455.0362854003906, 384.3638916015625, 469.7992858886719], "page": 12, "span": [0, 34], "__ref_s3_data": null}], "text": "3.3 VERIFY_GROUP_FOR_USER function", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 356.2593994140625, 547.2347412109375, 437.4712829589844], "page": 12, "span": [0, 576], "__ref_s3_data": null}], "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80001831054688, 310.2999572753906, 547.2573852539062, 343.5125732421875], "page": 12, "span": [0, 235], "__ref_s3_data": null}], "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80001831054688, 288.2803955078125, 458.44525146484375, 297.4933776855469], "page": 12, "span": [0, 63], "__ref_s3_data": null}], "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80001831054688, 271.2408142089844, 406.0775146484375, 280.45379638671875], "page": 12, "span": [0, 57], "__ref_s3_data": null}], "text": "- 1. There are user profiles for MGR, JANE, JUDY, and TONY.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.80001831054688, 254.26100158691406, 396.9881591796875, 263.4739990234375], "page": 12, "span": [0, 58], "__ref_s3_data": null}], "text": "- 2. The user profile JANE specifies a group profile of MGR.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.80001831054688, 225.28138732910156, 536.568603515625, 246.4941864013672], "page": 12, "span": [0, 127], "__ref_s3_data": null}], "text": "- 3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.20018005371094, 150.57144165039062, 451.01605224609375, 217.305419921875], "page": 12, "span": [0, 265], "__ref_s3_data": null}], "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [136.79959106445312, 711.5667724609375, 166.73934936523438, 720.341552734375], "page": 13, "span": [0, 6], "__ref_s3_data": null}], "text": "RETURN", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 699.5669555664062, 156.7793426513672, 708.3417358398438], "page": 13, "span": [0, 4], "__ref_s3_data": null}], "text": "CASE", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 531.5695190429688, 521.5742797851562, 696.3419189453125], "page": 13, "span": [0, 437], "__ref_s3_data": null}], "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [136.79959106445312, 495.2812805175781, 547.2122192382812, 516.4940795898438], "page": 13, "span": [0, 136], "__ref_s3_data": null}], "text": "- 2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.03939819335938, 478.3014831542969, 469.1528015136719, 487.51446533203125], "page": 13, "span": [0, 62], "__ref_s3_data": null}], "text": "- -Human Resources can see the unmasked TAX_ID of the employees.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.03939819335938, 461.26190185546875, 403.95953369140625, 470.4748840332031], "page": 13, "span": [0, 50], "__ref_s3_data": null}], "text": "- -Employees can see only their own unmasked TAX_ID.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.03939819335938, 432.28228759765625, 545.16845703125, 453.4950866699219], "page": 13, "span": [0, 129], "__ref_s3_data": null}], "text": "- -Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.03939819335938, 415.302490234375, 529.463623046875, 424.5154724121094], "page": 13, "span": [0, 77], "__ref_s3_data": null}], "text": "- -Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.1997833251953, 398.2629089355469, 530.060302734375, 407.47589111328125], "page": 13, "span": [0, 82], "__ref_s3_data": null}], "text": "- To implement this column mask, run the SQL statement that is shown in Example 3-9.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 377.156982421875, 351.9873046875, 385.48199462890625], "page": 13, "span": [0, 48], "__ref_s3_data": null}], "text": "Example 3-9 Creating a mask on the TAX_ID column", "type": "paragraph", "payload": null, "name": "paragraph", "font": null}, {"prov": [{"bbox": [136.8000030517578, 107.55116271972656, 526.5546875, 368.3218994140625], "page": 13, "span": [0, 590], "__ref_s3_data": null}], "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [344.94000244140625, 28.136999130249023, 523.6016235351562, 36.461997985839844], "page": 13, "span": [0, 40], "__ref_s3_data": null}], "text": "Chapter 3. Row and Column Access Control", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 13, "span": [0, 2], "__ref_s3_data": null}], "text": "27", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.8000030517578, 711.2783203125, 449.952392578125, 720.4913330078125], "page": 14, "span": [0, 65], "__ref_s3_data": null}], "text": "- 3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.80000305175781, 610.1370239257812, 293.1380920410156, 618.4619750976562], "page": 14, "span": [0, 52], "__ref_s3_data": null}], "text": "Figure 3-10 Column masks shown in System i Navigator", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/11"}, {"prov": [{"bbox": [64.80000305175781, 577.6372680664062, 203.98521423339844, 589.6253051757812], "page": 14, "span": [0, 21], "__ref_s3_data": null}], "text": "3.6.6 Activating RCAC", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 530.2586669921875, 547.2256469726562, 563.4713134765625], "page": 14, "span": [0, 265], "__ref_s3_data": null}], "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 513.2788696289062, 409.4788818359375, 522.4918823242188], "page": 14, "span": [0, 57], "__ref_s3_data": null}], "text": "- 1. Run the SQL statements that are shown in Example 3-10.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 492.11700439453125, 375.2909851074219, 500.4420166015625], "page": 14, "span": [0, 51], "__ref_s3_data": null}], "text": "Example 3-10 Activating RCAC on the EMPLOYEES table", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 474.5671081542969, 376.6766052246094, 483.3418884277344], "page": 14, "span": [0, 45], "__ref_s3_data": null}], "text": "- /* Active Row Access Control (permissions) */", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 462.5672912597656, 354.86962890625, 471.3420715332031], "page": 14, "span": [0, 39], "__ref_s3_data": null}], "text": "- /* Active Column Access Control (masks)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [365.77313232421875, 462.5672912597656, 376.6766052246094, 471.3420715332031], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "*/", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 450.5674743652344, 291.7178039550781, 459.3422546386719], "page": 14, "span": [0, 31], "__ref_s3_data": null}], "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 438.5676574707031, 271.6783142089844, 447.3424377441406], "page": 14, "span": [0, 27], "__ref_s3_data": null}], "text": "ACTIVATE ROW ACCESS CONTROL", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 426.5678405761719, 291.7178039550781, 435.3426208496094], "page": 14, "span": [0, 31], "__ref_s3_data": null}], "text": "ACTIVATE COLUMN ACCESS CONTROL;", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 378.27978515625, 540.8014526367188, 411.4924011230469], "page": 14, "span": [0, 231], "__ref_s3_data": null}], "text": "- 2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.80000305175781, 134.63710021972656, 347.4305419921875, 142.9621124267578], "page": 14, "span": [0, 65], "__ref_s3_data": null}], "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/12"}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 78.4020004272461, 37.15127944946289], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "28", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [93.42030334472656, 28.136999130249023, 334.4214172363281, 36.461997985839844], "page": 14, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.79959106445312, 687.2783203125, 514.048583984375, 720.490966796875], "page": 15, "span": [0, 228], "__ref_s3_data": null}], "text": "- 2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 303.11700439453125, 327.0932922363281, 311.4420166015625], "page": 15, "span": [0, 44], "__ref_s3_data": null}], "text": "Figure 4-68 Visual Explain with RCAC enabled", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/13"}, {"prov": [{"bbox": [136.8000030517578, 252.21875, 547.2394409179688, 285.4313659667969], "page": 15, "span": [0, 232], "__ref_s3_data": null}], "text": "- 3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.80000305175781, 116.15709686279297, 227.1014862060547, 124.48210144042969], "page": 15, "span": [0, 37], "__ref_s3_data": null}], "text": "Figure 4-69 Index advice with no RCAC", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/14"}, {"prov": [{"bbox": [214.8000030517578, 28.136999130249023, 523.5935668945312, 36.461997985839844], "page": 15, "span": [0, 70], "__ref_s3_data": null}], "text": "Chapter 4. Implementing Row and Column Access Control: Banking example", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 15, "span": [0, 2], "__ref_s3_data": null}], "text": "77", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80030822753906, 85.39237976074219, 500.697265625, 720.3270263671875], "page": 16, "span": [0, 1998], "__ref_s3_data": null}], "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 83.98200225830078, 37.15127944946289], "page": 16, "span": [0, 3], "__ref_s3_data": null}], "text": "124", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [98.94000244140625, 28.136999130249023, 339.819580078125, 36.461997985839844], "page": 16, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [287.2200012207031, 741.251953125, 414.24481201171875, 763.4519653320312], "page": 18, "span": [0, 10], "__ref_s3_data": null}], "text": "Back cover", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [27.0, 651.5399780273438, 447.3600158691406, 718.3619995117188], "page": 18, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [26.700000762939453, 525.1680297851562, 127.443603515625, 549.8280029296875], "page": 18, "span": [0, 40], "__ref_s3_data": null}], "text": "Implement roles and separation of duties", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [26.700000762939453, 469.1280212402344, 120.283203125, 507.8280334472656], "page": 18, "span": [0, 40], "__ref_s3_data": null}], "text": "Leverage row permissions on the database", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [26.700000762939453, 413.14801025390625, 121.44960021972656, 451.8480224609375], "page": 18, "span": [0, 40], "__ref_s3_data": null}], "text": "Protect columns by defining column masks", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [152.94000244140625, 468.4081115722656, 414.084228515625, 549.2714233398438], "page": 18, "span": [0, 464], "__ref_s3_data": null}], "text": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [152.9400177001953, 403.4290466308594, 414.173828125, 460.292724609375], "page": 18, "span": [0, 309], "__ref_s3_data": null}], "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [171.0, 152.3369903564453, 231.8876953125, 160.66200256347656], "page": 18, "span": [0, 12], "__ref_s3_data": null}], "text": "REDP-5110-00", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/15"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/16"}, {"prov": [{"bbox": [467.3399963378906, 489.8393859863281, 559.809326171875, 544.2816772460938], "page": 18, "span": [0, 44], "__ref_s3_data": null}], "text": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.3399963378906, 405.52801513671875, 587.38916015625, 440.2080078125], "page": 18, "span": [0, 60], "__ref_s3_data": null}], "text": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.3399963378906, 250.36593627929688, 587.5205078125, 392.13970947265625], "page": 18, "span": [0, 323], "__ref_s3_data": null}], "text": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.3399963378906, 190.48809814453125, 570.947998046875, 213.1680908203125], "page": 18, "span": [0, 39], "__ref_s3_data": null}], "text": "For more information: ibm.com /redbooks", "type": "paragraph", "payload": null, "name": "Text", "font": null}], "figures": [{"prov": [{"bbox": [513.4560546875, 737.1808471679688, 586.1583251953125, 765.9149169921875], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [33.090599060058594, 89.5469970703125, 585.1502075195312, 498.9671630859375], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [316.94049072265625, 17.57415771484375, 581.3547973632812, 81.8721923828125], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [143.39866638183594, 506.378662109375, 179.56256103515625, 521.7388916015625], "page": 3, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [64.16704559326172, 103.87176513671875, 258.77435302734375, 188.49365234375], "page": 3, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [142.52883911132812, 288.79351806640625, 251.47850036621094, 416.9550476074219], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [145.41445922851562, 156.616943359375, 252.08840942382812, 264.7552490234375], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [32.05510711669922, 553.9590454101562, 239.62696838378906, 721.5736694335938], "page": 5, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.92466735839844, 103.39019775390625, 546.4456176757812, 416.0727844238281], "page": 7, "span": [0, 43], "__ref_s3_data": null}], "text": "Figure 1-2 Existing row and column controls", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.9717559814453, 381.39068603515625, 545.4180297851562, 684.5892333984375], "page": 10, "span": [0, 177], "__ref_s3_data": null}], "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.Figure 3-1 CREATE PERMISSION SQL statement", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.64837646484375, 197.24334716796875, 301.2367248535156, 407.8263244628906], "page": 11, "span": [0, 50], "__ref_s3_data": null}], "text": "Figure 3-5 Special registers and adopted authority", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [63.80192184448242, 621.9678955078125, 547.11474609375, 696.6176147460938], "page": 14, "span": [0, 52], "__ref_s3_data": null}], "text": "Figure 3-10 Column masks shown in System i Navigator", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [63.985130310058594, 145.86041259765625, 530.0478515625, 364.0950012207031], "page": 14, "span": [0, 65], "__ref_s3_data": null}], "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [136.5016632080078, 314.45880126953125, 545.4508666992188, 672.7509155273438], "page": 15, "span": [0, 44], "__ref_s3_data": null}], "text": "Figure 4-68 Visual Explain with RCAC enabled", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [64.27847290039062, 127.91290283203125, 506.39263916015625, 238.41851806640625], "page": 15, "span": [0, 37], "__ref_s3_data": null}], "text": "Figure 4-69 Index advice with no RCAC", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [485.1698303222656, 737.8084106445312, 566.2962036132812, 766.7407836914062], "page": 18, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [474.35540771484375, 602.1873779296875, 592.2726440429688, 711.9486694335938], "page": 18, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [136.15110778808594, 76.34722900390625, 547.5270385742188, 659.9697265625], "page": 2, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table-of-contents", "payload": null, "#-cols": 2, "#-rows": 43, "data": [[{"bbox": [136.8000030517578, 650.1383666992188, 172.89404296875, 659.3513793945312], "spans": [[0, 0]], "text": "Notices", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [175.01951599121094, 650.1383666992188, 547.1898193359375, 659.3513793945312], "spans": [[0, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [136.79901123046875, 637.6585083007812, 189.86537170410156, 646.8715209960938], "spans": [[1, 0]], "text": "Trademarks", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [195.3968505859375, 637.6585083007812, 547.182861328125, 646.8715209960938], "spans": [[1, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [136.79901123046875, 615.1588745117188, 279.3973083496094, 624.3718872070312], "spans": [[2, 0]], "text": "DB2 for i Center of Excellence", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [280.6194152832031, 615.1588745117188, 547.1907958984375, 624.3718872070312], "spans": [[2, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [136.79901123046875, 592.6592407226562, 172.84423828125, 601.8722534179688], "spans": [[3, 0]], "text": "Preface", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [175.01852416992188, 592.6592407226562, 547.182861328125, 601.8722534179688], "spans": [[3, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [136.79803466796875, 580.1793823242188, 547.1808471679688, 589.3923950195312], "spans": [[4, 0]], "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": null, "spans": [[4, 1]], "text": "", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}], [{"bbox": [136.79803466796875, 567.6397705078125, 339.18292236328125, 576.852783203125], "spans": [[5, 0]], "text": "Now you can become a published author, too!", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [344.714111328125, 567.6397705078125, 547.1387939453125, 576.852783203125], "spans": [[5, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}], [{"bbox": [136.79803466796875, 555.159912109375, 529.9950561523438, 564.3729248046875], "spans": [[6, 0]], "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [535.5494995117188, 555.159912109375, 547.1978759765625, 564.3729248046875], "spans": [[6, 1]], "text": "xiii", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 6, "row-header": false, "row-span": [6, 7]}], [{"bbox": [136.79806518554688, 542.6800537109375, 284.0286560058594, 551.89306640625], "spans": [[7, 0]], "text": "Stay connected to IBM Redbooks", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": [289.54449462890625, 542.6800537109375, 547.1211547851562, 551.89306640625], "spans": [[7, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 7, "row-header": false, "row-span": [7, 8]}], [{"bbox": [136.79806518554688, 520.180419921875, 536.0958862304688, 529.3934326171875], "spans": [[8, 0]], "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": [541.6468505859375, 520.180419921875, 547.1978149414062, 529.3934326171875], "spans": [[8, 1]], "text": "1", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 8, "row-header": false, "row-span": [8, 9]}], [{"bbox": [136.79808044433594, 508.18060302734375, 549.8472290039062, 517.3936157226562], "spans": [[9, 0]], "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 1]], "text": "", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 9, "row-header": false, "row-span": [9, 10]}], [{"bbox": [136.79806518554688, 495.6409606933594, 536.1293334960938, 504.85394287109375], "spans": [[10, 0]], "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": [541.6611328125, 495.6409606933594, 547.19287109375, 504.85394287109375], "spans": [[10, 1]], "text": "2", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 10, "row-header": false, "row-span": [10, 11]}], [{"bbox": [136.79806518554688, 483.16107177734375, 549.8472290039062, 492.3740539550781], "spans": [[11, 0]], "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 1]], "text": "", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 11, "row-header": false, "row-span": [11, 12]}], [{"bbox": [151.19720458984375, 470.6811828613281, 536.0551147460938, 479.8941650390625], "spans": [[12, 0]], "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": [541.6015014648438, 470.6811828613281, 547.14794921875, 479.8941650390625], "spans": [[12, 1]], "text": "4", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 12, "row-header": false, "row-span": [12, 13]}], [{"bbox": [151.19720458984375, 458.14154052734375, 536.080078125, 467.3545227050781], "spans": [[13, 0]], "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 13, "row-header": false, "row-span": [13, 14]}, {"bbox": [541.635498046875, 458.14154052734375, 547.19091796875, 467.3545227050781], "spans": [[13, 1]], "text": "5", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 13, "row-header": false, "row-span": [13, 14]}], [{"bbox": [136.7970428466797, 435.64190673828125, 536.0908813476562, 444.8548889160156], "spans": [[14, 0]], "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 14, "row-header": false, "row-span": [14, 15]}, {"bbox": [541.642822265625, 435.64190673828125, 547.1947631835938, 444.8548889160156], "spans": [[14, 1]], "text": "7", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 14, "row-header": false, "row-span": [14, 15]}], [{"bbox": [136.7970428466797, 423.64208984375, 536.1271362304688, 432.8550720214844], "spans": [[15, 0]], "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 15, "row-header": false, "row-span": [15, 16]}, {"bbox": [541.6658935546875, 423.64208984375, 547.2047119140625, 432.8550720214844], "spans": [[15, 1]], "text": "8", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 15, "row-header": false, "row-span": [15, 16]}], [{"bbox": [151.19720458984375, 411.1622009277344, 535.9526977539062, 420.37518310546875], "spans": [[16, 0]], "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 16, "row-header": false, "row-span": [16, 17]}, {"bbox": [541.5558471679688, 411.1622009277344, 547.1590576171875, 420.37518310546875], "spans": [[16, 1]], "text": "8", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 16, "row-header": false, "row-span": [16, 17]}], [{"bbox": [151.19720458984375, 398.68231201171875, 536.0410766601562, 407.8952941894531], "spans": [[17, 0]], "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 17, "row-header": false, "row-span": [17, 18]}, {"bbox": [541.595947265625, 398.68231201171875, 547.1508178710938, 407.8952941894531], "spans": [[17, 1]], "text": "8", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 17, "row-header": false, "row-span": [17, 18]}], [{"bbox": [151.19720458984375, 386.1426696777344, 536.0748901367188, 395.35565185546875], "spans": [[18, 0]], "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 18, "row-header": false, "row-span": [18, 19]}, {"bbox": [541.6302490234375, 386.1426696777344, 547.1856079101562, 395.35565185546875], "spans": [[18, 1]], "text": "9", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 18, "row-header": false, "row-span": [18, 19]}], [{"bbox": [151.19720458984375, 373.66278076171875, 411.2704772949219, 382.8757629394531], "spans": [[19, 0]], "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 19, "row-header": false, "row-span": [19, 20]}, {"bbox": [416.8177490234375, 373.66278076171875, 547.1786499023438, 382.8757629394531], "spans": [[19, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 19, "row-header": false, "row-span": [19, 20]}], [{"bbox": [151.19720458984375, 361.1828918457031, 536.035888671875, 370.3958740234375], "spans": [[20, 0]], "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 20, "row-header": false, "row-span": [20, 21]}, {"bbox": [541.5989379882812, 361.1828918457031, 547.1619262695312, 370.3958740234375], "spans": [[20, 1]], "text": "9", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 20, "row-header": false, "row-span": [20, 21]}], [{"bbox": [151.19720458984375, 348.64324951171875, 530.5731811523438, 357.8562316894531], "spans": [[21, 0]], "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 21, "row-header": false, "row-span": [21, 22]}, {"bbox": [536.1044311523438, 348.64324951171875, 547.1668701171875, 357.8562316894531], "spans": [[21, 1]], "text": "10", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 21, "row-header": false, "row-span": [21, 22]}], [{"bbox": [151.19720458984375, 336.1633605957031, 530.5352172851562, 345.3763427734375], "spans": [[22, 0]], "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 22, "row-header": false, "row-span": [22, 23]}, {"bbox": [536.0755004882812, 336.1633605957031, 547.156005859375, 345.3763427734375], "spans": [[22, 1]], "text": "10", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 22, "row-header": false, "row-span": [22, 23]}], [{"bbox": [136.7970428466797, 323.6834716796875, 547.256591796875, 332.8964538574219], "spans": [[23, 0]], "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 23, "row-header": false, "row-span": [23, 24]}, {"bbox": null, "spans": [[23, 1]], "text": "", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 23, "row-header": false, "row-span": [23, 24]}], [{"bbox": [136.79702758789062, 301.183837890625, 530.5396118164062, 310.3968200683594], "spans": [[24, 0]], "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 24, "row-header": false, "row-span": [24, 25]}, {"bbox": [536.0916748046875, 301.183837890625, 547.19580078125, 310.3968200683594], "spans": [[24, 1]], "text": "13", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 24, "row-header": false, "row-span": [24, 25]}], [{"bbox": [136.79702758789062, 289.18402099609375, 530.4808959960938, 298.3970031738281], "spans": [[25, 0]], "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 25, "row-header": false, "row-span": [25, 26]}, {"bbox": [536.04248046875, 289.18402099609375, 547.1657104492188, 298.3970031738281], "spans": [[25, 1]], "text": "14", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 25, "row-header": false, "row-span": [25, 26]}], [{"bbox": [151.1971893310547, 276.6443786621094, 378.2078552246094, 285.85736083984375], "spans": [[26, 0]], "text": "3.1.1 Row permission and column mask definitions", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 26, "row-header": false, "row-span": [26, 27]}, {"bbox": [383.74713134765625, 276.6443786621094, 547.15576171875, 285.85736083984375], "spans": [[26, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 26, "row-header": false, "row-span": [26, 27]}], [{"bbox": [151.1971893310547, 264.16448974609375, 530.4347534179688, 273.3774719238281], "spans": [[27, 0]], "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 27, "row-header": false, "row-span": [27, 28]}, {"bbox": [535.9962158203125, 264.16448974609375, 547.1190795898438, 273.3774719238281], "spans": [[27, 1]], "text": "16", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 27, "row-header": false, "row-span": [27, 28]}], [{"bbox": [136.79702758789062, 251.6248321533203, 530.528076171875, 260.83782958984375], "spans": [[28, 0]], "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 28, "row-header": false, "row-span": [28, 29]}, {"bbox": [536.0670166015625, 251.6248321533203, 547.1448364257812, 260.83782958984375], "spans": [[28, 1]], "text": "18", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 28, "row-header": false, "row-span": [28, 29]}], [{"bbox": [151.1971893310547, 239.14495849609375, 530.4978637695312, 248.3579559326172], "spans": [[29, 0]], "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 29, "row-header": false, "row-span": [29, 30]}, {"bbox": [536.0518798828125, 239.14495849609375, 547.159912109375, 248.3579559326172], "spans": [[29, 1]], "text": "18", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 29, "row-header": false, "row-span": [29, 30]}], [{"bbox": [151.1971893310547, 226.6650848388672, 530.5602416992188, 235.87808227539062], "spans": [[30, 0]], "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 30, "row-header": false, "row-span": [30, 31]}, {"bbox": [536.09912109375, 226.6650848388672, 547.1768798828125, 235.87808227539062], "spans": [[30, 1]], "text": "19", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 30, "row-header": false, "row-span": [30, 31]}], [{"bbox": [136.79702758789062, 214.1254425048828, 530.5302734375, 223.33843994140625], "spans": [[31, 0]], "text": "3.3 VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 31, "row-header": false, "row-span": [31, 32]}, {"bbox": [536.0615234375, 214.1254425048828, 547.1240234375, 223.33843994140625], "spans": [[31, 1]], "text": "20", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 31, "row-header": false, "row-span": [31, 32]}], [{"bbox": [136.79702758789062, 201.64556884765625, 530.6299438476562, 210.8585662841797], "spans": [[32, 0]], "text": "3.4 Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 32, "row-header": false, "row-span": [32, 33]}, {"bbox": [536.1631469726562, 201.64556884765625, 547.2295532226562, 210.8585662841797], "spans": [[32, 1]], "text": "21", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 32, "row-header": false, "row-span": [32, 33]}], [{"bbox": [400.3206481933594, 189.1656951904297, 530.4835815429688, 198.37869262695312], "spans": [[33, 0]], "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 33, "row-header": false, "row-span": [33, 34]}, {"bbox": [536.0223999023438, 189.1656951904297, 547.10009765625, 198.37869262695312], "spans": [[33, 1]], "text": "22", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 33, "row-header": false, "row-span": [33, 34]}], [{"bbox": [136.79701232910156, 176.6260528564453, 530.5651245117188, 198.37869262695312], "spans": [[34, 0]], "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC 3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 34, "row-header": false, "row-span": [34, 35]}, {"bbox": [536.1119995117188, 176.6260528564453, 547.2057495117188, 185.83905029296875], "spans": [[34, 1]], "text": "22", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 34, "row-header": false, "row-span": [34, 35]}], [{"bbox": [151.19717407226562, 164.14617919921875, 530.4913940429688, 173.3591766357422], "spans": [[35, 0]], "text": "3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 35, "row-header": false, "row-span": [35, 36]}, {"bbox": [536.0463256835938, 164.14617919921875, 547.1561889648438, 173.3591766357422], "spans": [[35, 1]], "text": "23", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 35, "row-header": false, "row-span": [35, 36]}], [{"bbox": [151.19717407226562, 151.6663055419922, 530.5645751953125, 160.87930297851562], "spans": [[36, 0]], "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 36, "row-header": false, "row-span": [36, 37]}, {"bbox": [536.0960083007812, 151.6663055419922, 547.1587524414062, 160.87930297851562], "spans": [[36, 1]], "text": "23", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 36, "row-header": false, "row-span": [36, 37]}], [{"bbox": [151.19717407226562, 139.1266632080078, 530.5569458007812, 148.33966064453125], "spans": [[37, 0]], "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 37, "row-header": false, "row-span": [37, 38]}, {"bbox": [536.0881958007812, 139.1266632080078, 547.1507568359375, 148.33966064453125], "spans": [[37, 1]], "text": "24", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 37, "row-header": false, "row-span": [37, 38]}], [{"bbox": [151.19717407226562, 126.64678955078125, 530.5341186523438, 135.8597869873047], "spans": [[38, 0]], "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 38, "row-header": false, "row-span": [38, 39]}, {"bbox": [536.072998046875, 126.64678955078125, 547.15087890625, 135.8597869873047], "spans": [[38, 1]], "text": "25", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 38, "row-header": false, "row-span": [38, 39]}], [{"bbox": [151.19717407226562, 114.16690826416016, 339.4510498046875, 123.37991333007812], "spans": [[39, 0]], "text": "3.6.5 Defining and creating column masks", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 39, "row-header": false, "row-span": [39, 40]}, {"bbox": [344.9899597167969, 114.16690826416016, 547.160888671875, 123.37991333007812], "spans": [[39, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 39, "row-header": false, "row-span": [39, 40]}], [{"bbox": [151.19717407226562, 101.62727355957031, 530.541015625, 110.84027099609375], "spans": [[40, 0]], "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 40, "row-header": false, "row-span": [40, 41]}, {"bbox": [536.087646484375, 101.62727355957031, 547.1808471679688, 110.84027099609375], "spans": [[40, 1]], "text": "28", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 40, "row-header": false, "row-span": [40, 41]}], [{"bbox": [151.19717407226562, 89.14738464355469, 530.5750732421875, 98.36038970947266], "spans": [[41, 0]], "text": "3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 41, "row-header": false, "row-span": [41, 42]}, {"bbox": [536.1066284179688, 89.14738464355469, 547.169677734375, 98.36038970947266], "spans": [[41, 1]], "text": "29", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 41, "row-header": false, "row-span": [41, 42]}], [{"bbox": [151.19717407226562, 76.6675033569336, 530.436279296875, 85.88050842285156], "spans": [[42, 0]], "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 42, "row-header": false, "row-span": [42, 43]}, {"bbox": [535.9984741210938, 76.6675033569336, 547.1228637695312, 85.88050842285156], "spans": [[42, 1]], "text": "32", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 42, "row-header": false, "row-span": [42, 43]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [135.5250701904297, 349.9494934082031, 545.87060546875, 502.2750549316406], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 2-1 FUNCTION_USAGE view", "type": "table", "payload": null, "#-cols": 3, "#-rows": 5, "data": [[{"bbox": [142.8000030517578, 487.1369934082031, 202.2449951171875, 495.4620056152344], "spans": [[0, 0]], "text": "Column name", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [216.8087921142578, 487.1369934082031, 257.210693359375, 495.4620056152344], "spans": [[0, 1]], "text": "Data type", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [289.47479248046875, 487.1369934082031, 338.8946838378906, 495.4620056152344], "spans": [[0, 2]], "text": "Description", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [142.8000030517578, 468.1172790527344, 203.2322998046875, 476.4422912597656], "spans": [[1, 0]], "text": "FUNCTION_ID", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [216.785400390625, 468.1172790527344, 276.00360107421875, 476.4422912597656], "spans": [[1, 1]], "text": "VARCHAR(30)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [289.45770263671875, 468.1172790527344, 359.85394287109375, 476.4422912597656], "spans": [[1, 2]], "text": "ID of the function.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [142.8000030517578, 449.156982421875, 198.66929626464844, 457.48199462890625], "spans": [[2, 0]], "text": "USER_NAME", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [216.74130249023438, 449.156982421875, 275.9234924316406, 457.48199462890625], "spans": [[2, 1]], "text": "VARCHAR(10)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [289.382080078125, 438.1166687011719, 515.0535888671875, 457.48199462890625], "spans": [[2, 2]], "text": "Name of the user profile that has a usage setting for this  function.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [142.79998779296875, 419.1563720703125, 173.98318481445312, 427.48138427734375], "spans": [[3, 0]], "text": "USAGE", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [216.773681640625, 419.1563720703125, 270.9797668457031, 427.48138427734375], "spans": [[3, 1]], "text": "VARCHAR(7)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [289.416259765625, 397.13604736328125, 539.1071166992188, 427.48138427734375], "spans": [[3, 2]], "text": "Usage setting: GLYPH<SM590000> ALLOWED: The user profile is allowed to use the function. GLYPH<SM590000> DENIED: The user profile is not allowed to use the function.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [142.8000030517578, 378.1163330078125, 196.2248992919922, 386.44134521484375], "spans": [[4, 0]], "text": "USER_TYPE", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [216.75210571289062, 378.1163330078125, 270.99871826171875, 386.44134521484375], "spans": [[4, 1]], "text": "VARCHAR(5)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [289.4316101074219, 356.15631103515625, 448.11962890625, 386.44134521484375], "spans": [[4, 2]], "text": "Type of user profile: GLYPH<SM590000> USER: The user profile is a user. GLYPH<SM590000> GROUP: The user profile is a group.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 4, "row-header": false, "row-span": [4, 5]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [64.41139221191406, 70.39208984375, 547.3950805664062, 398.3863830566406], "page": 9, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "type": "table", "payload": null, "#-cols": 6, "#-rows": 13, "data": [[{"bbox": [70.80030059814453, 383.1567077636719, 119.78550720214844, 391.4817199707031], "spans": [[0, 0]], "text": "User action", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 0, "row-header": true, "row-span": [0, 1]}, {"bbox": [424.93804931640625, 304.9800109863281, 433.2629699707031, 344.4774475097656], "spans": [[0, 1]], "text": "*JOBCTL", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [450.1380615234375, 304.9800109863281, 458.4629821777344, 390.3999328613281], "spans": [[0, 2]], "text": "QIBM_DB_SECADM", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [475.9383544921875, 304.9800109863281, 484.2632751464844, 390.465576171875], "spans": [[0, 3]], "text": "QIBM_DB_SQLADM", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [501.13836669921875, 304.9800109863281, 509.4632873535156, 390.385498046875], "spans": [[0, 4]], "text": "QIBM_DB_SYSMON", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [526.3986206054688, 304.9799499511719, 534.7235717773438, 359.2005615234375], "spans": [[0, 5]], "text": "No Authority", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [70.80000305175781, 285.11700439453125, 220.1568145751953, 293.4420166015625], "spans": [[1, 0]], "text": "SET CURRENT DEGREE  (SQL statement)", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": true, "row-span": [1, 2]}, {"bbox": [429.0, 285.11700439453125, 435.00299072265625, 293.4420166015625], "spans": [[1, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": null, "spans": [[1, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [480.00030517578125, 285.11700439453125, 486.0032958984375, 293.4420166015625], "spans": [[1, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": null, "spans": [[1, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": null, "spans": [[1, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [70.80001831054688, 266.1567077636719, 264.5538024902344, 274.4817199707031], "spans": [[2, 0]], "text": "CHGQRYA  command targeting a different user's job", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": true, "row-span": [2, 3]}, {"bbox": [429.0000305175781, 266.1567077636719, 435.0030212402344, 274.4817199707031], "spans": [[2, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": null, "spans": [[2, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [480.0003356933594, 266.1567077636719, 486.0033264160156, 274.4817199707031], "spans": [[2, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": null, "spans": [[2, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": null, "spans": [[2, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [70.800048828125, 247.1370086669922, 322.5057373046875, 255.46202087402344], "spans": [[3, 0]], "text": "STRDBMON  or  ENDDBMON  commands targeting a different user's job", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": true, "row-span": [3, 4]}, {"bbox": [429.0000305175781, 247.1370086669922, 435.0030212402344, 255.46202087402344], "spans": [[3, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": null, "spans": [[3, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [480.0003356933594, 247.1370086669922, 486.0033264160156, 255.46202087402344], "spans": [[3, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": null, "spans": [[3, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": null, "spans": [[3, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [70.800048828125, 228.1173095703125, 381.0218505859375, 236.44232177734375], "spans": [[4, 0]], "text": "STRDBMON  or  ENDDBMON  commands targeting a job that matches the current user", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": true, "row-span": [4, 5]}, {"bbox": [429.0000305175781, 228.1173095703125, 435.0030212402344, 236.44232177734375], "spans": [[4, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": null, "spans": [[4, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [480.0003356933594, 228.1173095703125, 486.0033264160156, 236.44232177734375], "spans": [[4, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [505.2606201171875, 228.1173095703125, 511.26361083984375, 236.44232177734375], "spans": [[4, 4]], "text": "X", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [530.7603149414062, 228.1173095703125, 536.7633056640625, 236.44232177734375], "spans": [[4, 5]], "text": "X", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 4, "row-header": false, "row-span": [4, 5]}], [{"bbox": [70.800048828125, 209.15701293945312, 359.5173645019531, 217.48202514648438], "spans": [[5, 0]], "text": "QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": true, "row-span": [5, 6]}, {"bbox": [429.00006103515625, 209.15701293945312, 435.0030517578125, 217.48202514648438], "spans": [[5, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": null, "spans": [[5, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [480.0003662109375, 209.15701293945312, 486.00335693359375, 217.48202514648438], "spans": [[5, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [505.26068115234375, 209.15701293945312, 511.263671875, 217.48202514648438], "spans": [[5, 4]], "text": "X", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": null, "spans": [[5, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 5, "row-header": false, "row-span": [5, 6]}], [{"bbox": [70.80007934570312, 190.13731384277344, 220.7517852783203, 198.4623260498047], "spans": [[6, 0]], "text": "Visual Explain within Run SQL scripts", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 6, "row-header": true, "row-span": [6, 7]}, {"bbox": [429.00006103515625, 190.13731384277344, 435.0030517578125, 198.4623260498047], "spans": [[6, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": null, "spans": [[6, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [480.0003662109375, 190.13731384277344, 486.00335693359375, 198.4623260498047], "spans": [[6, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [505.26068115234375, 190.13731384277344, 511.263671875, 198.4623260498047], "spans": [[6, 4]], "text": "X", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [530.7603759765625, 190.13731384277344, 536.7633666992188, 198.4623260498047], "spans": [[6, 5]], "text": "X", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 6, "row-header": false, "row-span": [6, 7]}], [{"bbox": [70.80007934570312, 171.11761474609375, 236.65480041503906, 179.442626953125], "spans": [[7, 0]], "text": "Visual Explain outside of Run SQL scripts", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 7, "row-header": true, "row-span": [7, 8]}, {"bbox": [429.00006103515625, 171.11761474609375, 435.0030517578125, 179.442626953125], "spans": [[7, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": null, "spans": [[7, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": [480.0003662109375, 171.11761474609375, 486.00335693359375, 179.442626953125], "spans": [[7, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": null, "spans": [[7, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": null, "spans": [[7, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 7, "row-header": false, "row-span": [7, 8]}], [{"bbox": [70.80007934570312, 152.15731811523438, 213.1296844482422, 160.48233032226562], "spans": [[8, 0]], "text": "ANALYZE PLAN CACHE procedure", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 8, "row-header": true, "row-span": [8, 9]}, {"bbox": [429.00006103515625, 152.15731811523438, 435.0030517578125, 160.48233032226562], "spans": [[8, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": null, "spans": [[8, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": [480.0003662109375, 152.15731811523438, 486.00335693359375, 160.48233032226562], "spans": [[8, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": null, "spans": [[8, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": null, "spans": [[8, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 8, "row-header": false, "row-span": [8, 9]}], [{"bbox": [70.80007934570312, 133.1376190185547, 199.87808227539062, 141.46263122558594], "spans": [[9, 0]], "text": "DUMP PLAN CACHE procedure", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 9, "row-header": true, "row-span": [9, 10]}, {"bbox": [429.00006103515625, 133.1376190185547, 435.0030517578125, 141.46263122558594], "spans": [[9, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": [480.0003662109375, 133.1376190185547, 486.00335693359375, 141.46263122558594], "spans": [[9, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 9, "row-header": false, "row-span": [9, 10]}], [{"bbox": [70.80007934570312, 114.11792755126953, 208.36776733398438, 122.44291687011719], "spans": [[10, 0]], "text": "MODIFY PLAN CACHE procedure", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 10, "row-header": true, "row-span": [10, 11]}, {"bbox": [429.00006103515625, 114.11792755126953, 435.0030517578125, 122.44291687011719], "spans": [[10, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": null, "spans": [[10, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": [480.0003662109375, 114.11792755126953, 486.00335693359375, 122.44291687011719], "spans": [[10, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": null, "spans": [[10, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": null, "spans": [[10, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 10, "row-header": false, "row-span": [10, 11]}], [{"bbox": [70.80007934570312, 95.09822845458984, 411.20263671875, 103.42323303222656], "spans": [[11, 0]], "text": "MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority)", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 11, "row-header": true, "row-span": [11, 12]}, {"bbox": [429.00006103515625, 95.09822845458984, 435.0030517578125, 103.42323303222656], "spans": [[11, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": [480.0003662109375, 95.09822845458984, 486.00335693359375, 103.42323303222656], "spans": [[11, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 11, "row-header": false, "row-span": [11, 12]}], [{"bbox": [70.80007934570312, 76.13793182373047, 377.1258544921875, 84.46292877197266], "spans": [[12, 0]], "text": "CHANGE PLAN CACHE SIZE procedure (currently does not check authority)", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 12, "row-header": true, "row-span": [12, 13]}, {"bbox": [429.00006103515625, 76.13793182373047, 435.0030517578125, 84.46292877197266], "spans": [[12, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": null, "spans": [[12, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": [480.0003662109375, 76.13793182373047, 486.00335693359375, 84.46292877197266], "spans": [[12, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": null, "spans": [[12, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": null, "spans": [[12, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 12, "row-header": false, "row-span": [12, 13]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [134.5463104248047, 587.7283935546875, 542.0460205078125, 688.5811157226562], "page": 11, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 3-1 Special registers and their corresponding values", "type": "table", "payload": null, "#-cols": 2, "#-rows": 4, "data": [[{"bbox": [142.8000030517578, 673.1370239257812, 209.67091369628906, 681.4619750976562], "spans": [[0, 0]], "text": "Special register", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [230.18911743164062, 673.1370239257812, 319.9352722167969, 681.4619750976562], "spans": [[0, 1]], "text": "Corresponding value", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [142.80001831054688, 643.1364135742188, 212.7012176513672, 662.5016479492188], "spans": [[1, 0]], "text": "USER or SESSION_USER", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [230.2197265625, 654.1766967773438, 467.9906921386719, 662.5016479492188], "spans": [[1, 1]], "text": "The effective user of the thread excluding adopted authority.", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [142.80003356933594, 624.11669921875, 216.63963317871094, 632.441650390625], "spans": [[2, 0]], "text": "CURRENT_USER", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [230.19813537597656, 613.13671875, 535.6508178710938, 632.441650390625], "spans": [[2, 1]], "text": "The effective user of the thread including adopted authority. When no adopted  authority is present, this has the same value as USER.", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [142.8009033203125, 594.1170043945312, 209.73570251464844, 602.4419555664062], "spans": [[3, 0]], "text": "SYSTEM_USER", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [230.24490356445312, 594.1170043945312, 425.64569091796875, 602.4419555664062], "spans": [[3, 1]], "text": "The authorization ID that initiated the connection.", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [63.55636978149414, 495.77532958984375, 548.5687255859375, 687.76611328125], "page": 12, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 3-2 Built-in global variables", "type": "table", "payload": null, "#-cols": 3, "#-rows": 10, "data": [[{"bbox": [70.80000305175781, 673.1370239257812, 134.99070739746094, 681.4619750976562], "spans": [[0, 0]], "text": "Global variable", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [202.889404296875, 673.1370239257812, 223.34640502929688, 681.4619750976562], "spans": [[0, 1]], "text": "Type", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [281.8247985839844, 673.1370239257812, 331.3428039550781, 681.4619750976562], "spans": [[0, 2]], "text": "Description", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [70.80000305175781, 654.1766967773438, 132.7209014892578, 662.5016479492188], "spans": [[1, 0]], "text": "CLIENT_HOST", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [202.89028930664062, 654.1766967773438, 267.0765075683594, 662.5016479492188], "spans": [[1, 1]], "text": "VARCHAR(255)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [281.8473205566406, 654.1766967773438, 510.17547607421875, 662.5016479492188], "spans": [[1, 2]], "text": "Host name of the current client as returned by the system", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [70.80001831054688, 635.156982421875, 140.66522216796875, 643.48193359375], "spans": [[2, 0]], "text": "CLIENT_IPADDR", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [202.872314453125, 635.156982421875, 267.077392578125, 643.48193359375], "spans": [[2, 1]], "text": "VARCHAR(128)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [281.8454895019531, 635.156982421875, 509.6058349609375, 643.48193359375], "spans": [[2, 2]], "text": "IP address of the current client as returned by the system", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [70.80001831054688, 616.1372680664062, 134.98263549804688, 624.4622192382812], "spans": [[3, 0]], "text": "CLIENT_PORT", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [202.90293884277344, 616.1372680664062, 242.80084228515625, 624.4622192382812], "spans": [[3, 1]], "text": "INTEGER", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [281.7978515625, 616.1372680664062, 527.5922241210938, 624.4622192382812], "spans": [[3, 2]], "text": "Port used by the current client to communicate with the server", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [70.80001831054688, 597.1175537109375, 143.50924682617188, 605.4425048828125], "spans": [[4, 0]], "text": "PACKAGE_NAME", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [202.80575561523438, 597.1175537109375, 267.0693664550781, 605.4425048828125], "spans": [[4, 1]], "text": "VARCHAR(128)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [281.85186767578125, 597.1175537109375, 436.5726013183594, 605.4425048828125], "spans": [[4, 2]], "text": "Name of the currently running package", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 4, "row-header": false, "row-span": [4, 5]}], [{"bbox": [70.80001831054688, 578.1572265625, 156.01654052734375, 586.482177734375], "spans": [[5, 0]], "text": "PACKAGE_SCHEMA", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [202.83544921875, 578.1572265625, 267.0864562988281, 586.482177734375], "spans": [[5, 1]], "text": "VARCHAR(128)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [281.8707580566406, 578.1572265625, 470.44677734375, 586.482177734375], "spans": [[5, 2]], "text": "Schema name of the currently running package", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 5, "row-header": false, "row-span": [5, 6]}], [{"bbox": [70.80001831054688, 559.1375122070312, 157.89932250976562, 567.4624633789062], "spans": [[6, 0]], "text": "PACKAGE_VERSION", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [202.72471618652344, 559.1375122070312, 261.9825439453125, 567.4624633789062], "spans": [[6, 1]], "text": "VARCHAR(64)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [281.7492370605469, 559.1375122070312, 478.84381103515625, 567.4624633789062], "spans": [[6, 2]], "text": "Version identifier of the currently running package", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 6, "row-header": false, "row-span": [6, 7]}], [{"bbox": [70.80001831054688, 540.1177978515625, 154.419921875, 548.4427490234375], "spans": [[7, 0]], "text": "ROUTINE_SCHEMA", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": [202.79312133789062, 540.1177978515625, 267.0927429199219, 548.4427490234375], "spans": [[7, 1]], "text": "VARCHAR(128)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 7, "row-header": 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diff --git a/tests/data/groundtruth/docling_v1/redp5110_sampled.md b/tests/data/groundtruth/docling_v1/redp5110_sampled.md
index 48a3a13b..fb370361 100644
--- a/tests/data/groundtruth/docling_v1/redp5110_sampled.md
+++ b/tests/data/groundtruth/docling_v1/redp5110_sampled.md
@@ -216,20 +216,20 @@ Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL aut
 
 Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority
 
-| User action                                                                    | *JOBCTL   | QIBM_DB_SECADM   | QIBM_DB_SQLADM   | QIBM_DB_SYSMON No Authority   |
-|--------------------------------------------------------------------------------|-----------|------------------|------------------|-------------------------------|
-| SET CURRENT DEGREE  (SQL statement)                                            | X         |                  | X                |                               |
-| CHGQRYA  command targeting a different user's job                              | X         |                  | X                |                               |
-| STRDBMON  or  ENDDBMON  commands targeting a different user's job              | X         |                  | X                |                               |
-| STRDBMON  or  ENDDBMON  commands targeting a job that matches the current user | X         |                  | X                | X X                           |
-| QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job          | X         |                  | X                | X                             |
-| Visual Explain within Run SQL scripts                                          | X         |                  | X                | X X                           |
-| Visual Explain outside of Run SQL scripts                                      | X         |                  | X                |                               |
-| ANALYZE PLAN CACHE procedure                                                   | X         |                  | X                |                               |
-| DUMP PLAN CACHE procedure                                                      | X         |                  | X                |                               |
-| MODIFY PLAN CACHE procedure                                                    | X         |                  | X                |                               |
-| MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority)    | X         |                  | X                |                               |
-| CHANGE PLAN CACHE SIZE procedure (currently does not check authority)          | X         |                  | X                |                               |
+| User action                                                                    | *JOBCTL   | QIBM_DB_SECADM   | QIBM_DB_SQLADM   | QIBM_DB_SYSMON   | No Authority   |
+|--------------------------------------------------------------------------------|-----------|------------------|------------------|------------------|----------------|
+| SET CURRENT DEGREE  (SQL statement)                                            | X         |                  | X                |                  |                |
+| CHGQRYA  command targeting a different user's job                              | X         |                  | X                |                  |                |
+| STRDBMON  or  ENDDBMON  commands targeting a different user's job              | X         |                  | X                |                  |                |
+| STRDBMON  or  ENDDBMON  commands targeting a job that matches the current user | X         |                  | X                | X                | X              |
+| QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job          | X         |                  | X                | X                |                |
+| Visual Explain within Run SQL scripts                                          | X         |                  | X                | X                | X              |
+| Visual Explain outside of Run SQL scripts                                      | X         |                  | X                |                  |                |
+| ANALYZE PLAN CACHE procedure                                                   | X         |                  | X                |                  |                |
+| DUMP PLAN CACHE procedure                                                      | X         |                  | X                |                  |                |
+| MODIFY PLAN CACHE procedure                                                    | X         |                  | X                |                  |                |
+| MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority)    | X         |                  | X                |                  |                |
+| CHANGE PLAN CACHE SIZE procedure (currently does not check authority)          | X         |                  | X                |                  |                |
 
 The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.
 
diff --git a/tests/data/groundtruth/docling_v1/redp5110_sampled.pages.json b/tests/data/groundtruth/docling_v1/redp5110_sampled.pages.json
index a2dea0f8..43a46879 100644
--- a/tests/data/groundtruth/docling_v1/redp5110_sampled.pages.json
+++ b/tests/data/groundtruth/docling_v1/redp5110_sampled.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "ibm.com", "bbox": {"l": 36.900002, "t": 751.23, "r": 98.600998, "b": 765.105, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "/redbooks", "bbox": {"l": 98.580002, "t": 751.23, "r": 164.4585, "b": 765.105, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Redpaper", "bbox": {"l": 314.70001, "t": 711.50856, "r": 580.52002, "b": 773.772959, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Front cover", "bbox": {"l": 287.82001, "t": 28.54803000000004, "r": 418.83356, "b": 50.748050000000035, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Row and Column Access Control ", "bbox": {"l": 35.700001, "t": 84.58654999999987, "r": 584.64288, "b": 122.82097999999996, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Support in IBM DB2 for i", "bbox": {"l": 35.700001, "t": 127.60668999999996, "r": 447.66919000000007, "b": 165.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Jim Bainbridge", "bbox": {"l": 509.22, "t": 575.71201, "r": 581.34119, "b": 586.392, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Hernando Bedoya", "bbox": {"l": 497.70001, "t": 589.752, "r": 581.3736, "b": 600.43199, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Rob Bestgen", "bbox": {"l": 521.40002, "t": 603.73199, "r": 581.34003, "b": 614.4119900000001, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Mike Cain", "bbox": {"l": 534.12, "t": 617.71199, "r": 581.31598, "b": 628.39198, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Dan Cruikshank", "bbox": {"l": 505.43999999999994, "t": 631.75198, "r": 581.29077, "b": 642.4319800000001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Jim Denton", "bbox": {"l": 527.22003, "t": 645.73198, "r": 581.29926, "b": 656.41197, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Doug Mack", "bbox": {"l": 527.64001, "t": 659.71198, "r": 581.30884, "b": 670.39197, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Tom McKinley", "bbox": {"l": 514.38, "t": 673.75197, "r": 581.38678, "b": 684.43197, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Kent Milligan", "bbox": {"l": 518.82001, "t": 687.73197, "r": 581.33759, "b": 698.411972, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Implement roles and separation ", "bbox": {"l": 36.119999, "t": 296.13828, "r": 216.00064, "b": 308.58047, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of duties", "bbox": {"l": 36.119999, "t": 313.11838000000006, "r": 84.527145, "b": 325.56058, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Leverage", "bbox": {"l": 35.759315, "t": 350.13881999999995, "r": 80.409691, "b": 362.58102, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "row permissions on ", "bbox": {"l": 151.8503, "t": 350.13881999999995, "r": 202.45404, "b": 362.58102, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the", "bbox": {"l": 35.759315, "t": 367.11893, "r": 50.758106, "b": 379.56112999999993, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "database", "bbox": {"l": 86.755211, "t": 367.11893, "r": 107.75352000000001, "b": 379.56112999999993, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Protect", "bbox": {"l": 36.059887, "t": 404.13937, "r": 73.020607, "b": 416.58157, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "columns by defining ", "bbox": {"l": 152.62831, "t": 404.13937, "r": 195.2753, "b": 416.58157, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "column", "bbox": {"l": 36.059887, "t": 421.11948, "r": 73.311806, "b": 433.56167999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "masks", "bbox": {"l": 73.311806, "t": 421.11948, "r": 117.33681, "b": 433.56167999999997, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 0, "label": "picture", "bbox": {"l": 513.4560546875, "t": 26.085105895996094, "r": 586.1583251953125, "b": 54.819149017333984, "coord_origin": "TOPLEFT"}, "confidence": 0.9272358417510986, "cells": [], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 287.82001, "t": 28.54803000000004, "r": 418.83356, "b": 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"coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the", "bbox": {"l": 35.759315, "t": 367.11893, "r": 50.758106, "b": 379.56112999999993, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "database", "bbox": {"l": 86.755211, "t": 367.11893, "r": 107.75352000000001, "b": 379.56112999999993, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 36.059887, "t": 404.13937, "r": 195.2753, "b": 433.56167999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8421136140823364, "cells": [{"id": 21, "text": "Protect", "bbox": {"l": 36.059887, "t": 404.13937, "r": 73.020607, "b": 416.58157, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "columns by defining ", "bbox": {"l": 152.62831, "t": 404.13937, "r": 195.2753, "b": 416.58157, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "column", "bbox": {"l": 36.059887, "t": 421.11948, "r": 73.311806, "b": 433.56167999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "masks", "bbox": {"l": 73.311806, "t": 421.11948, 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 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"bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "3.6.4", "bbox": {"l": 151.19717, "t": 656.14021, "r": 173.35289, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89182, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "25", "bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "3.6.5", "bbox": {"l": 151.19717, "t": 668.62009, "r": 173.35289, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Defining and creating column masks", "bbox": {"l": 178.89182, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.98996, "t": 668.62009, "r": 530.54413, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "26", "bbox": {"l": 536.08301, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "3.6.6", "bbox": {"l": 151.19717, "t": 681.15973, "r": 173.38359, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.93019, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "28", "bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3.6.7", "bbox": {"l": 151.19717, "t": 693.63961, "r": 173.32332, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Demonstrating data access with RCAC", "bbox": {"l": 178.85486, "t": 693.63961, "r": 350.80011, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 356.33163, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "29", "bbox": {"l": 536.10663, "t": 693.63961, "r": 547.16968, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "3.6.8", "bbox": {"l": 151.19717, "t": 706.119492, "r": 173.44592, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00812, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "32", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "document_index", "bbox": {"l": 136.79701, "t": 132.64862000000005, "r": 549.84723, "b": 715.332497, "coord_origin": "TOPLEFT"}, "confidence": 0.9803490042686462, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3.6.2", "bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 173.32271, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8541, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "23", "bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "3.6.3", "bbox": {"l": 151.19717, "t": 643.66034, "r": 173.32227, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.85353, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "24", "bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "3.6.4", "bbox": {"l": 151.19717, "t": 656.14021, "r": 173.35289, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89182, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "25", "bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "3.6.5", "bbox": {"l": 151.19717, "t": 668.62009, "r": 173.35289, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Defining and creating column masks", "bbox": {"l": 178.89182, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.98996, "t": 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"cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.79701, "t": 132.64862000000005, "r": 549.84723, "b": 715.332497, "coord_origin": "TOPLEFT"}, "confidence": 0.9803490042686462, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 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. . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and 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1, "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0882, "t": 643.66034, "r": 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false, "row_section": false}]}}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Contents"}, {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.79701, "t": 132.64862000000005, "r": 549.84723, "b": 715.332497, "coord_origin": "TOPLEFT"}, "confidence": 0.9803490042686462, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3.6.2", "bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 173.32271, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8541, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "23", "bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "3.6.3", "bbox": {"l": 151.19717, "t": 643.66034, "r": 173.32227, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.85353, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "24", 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"b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 30, "end_row_offset_idx": 31, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 30, "end_row_offset_idx": 31, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "19", "column_header": false, "row_header": false, 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"row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . 22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79701, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, 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1, "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "24", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "25", "column_header": false, "row_header": false, 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"end_col_offset_idx": 1, "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "28", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10663, "t": 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false, "row_section": false}]}, {"label": "page_footer", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.892595112323761, "cells": [{"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "iii"}, {"label": "page_footer", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9473134279251099, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}], "body": [{"label": "section_header", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Contents"}, {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.79701, "t": 132.64862000000005, "r": 549.84723, "b": 715.332497, "coord_origin": "TOPLEFT"}, "confidence": 0.9803490042686462, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 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. . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Preface", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 542.91888, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "DB2fi for i Row and Column Access Control (RCAC). 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A solid background in IBM i object level security, DB2 for i relational ", "bbox": {"l": 136.79987, "t": 238.60712, "r": 521.25488, "b": 247.82012999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "database concepts, and SQL is assumed.", "bbox": {"l": 136.79987, "t": 250.60693000000003, "r": 321.69434, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. 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He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. 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His areas of expertise are database technology, ", "bbox": {"l": 263.3996, "t": 647.62454, "r": 524.77386, "b": 656.83754, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "performance, and data warehousing. Hernando can be ", "bbox": {"l": 263.3996, "t": 659.62434, "r": 508.27124, "b": 668.83735, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "contacted at ", "bbox": {"l": 263.3996, "t": 671.62415, "r": 320.63568, "b": 680.83716, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "hbedoya@us.ibm.com", "bbox": {"l": 320.63971, "t": 671.77356, "r": 410.57852, "b": 680.54832, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": ".", "bbox": {"l": 410.5795, "t": 671.62415, "r": 413.34839, "b": 680.83716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master\u2019s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"label": "page_footer", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9040942788124084, "cells": [{"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "xi"}, {"label": "page_footer", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469243884086609, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}], "body": [{"label": "section_header", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9174709916114807, "cells": [{"id": 2, "text": "Preface", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preface"}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79984, "t": 132.64862000000005, "r": 547.30823, "b": 201.86072000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9869155883789062, "cells": [{"id": 3, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 542.91888, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "bbox": {"l": 136.79984, "t": 144.64844000000005, "r": 526.65509, "b": 153.86145, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "bbox": {"l": 136.79984, "t": 156.64824999999996, "r": 536.82135, "b": 165.86127, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "bbox": {"l": 136.79987, "t": 168.64806999999996, "r": 544.67975, "b": 177.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "of defining, creating, and implementing the row permissions and column masks in a relational ", "bbox": {"l": 136.79987, "t": 180.64788999999996, "r": 547.30823, "b": 189.86090000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "database environment.", "bbox": {"l": 136.79987, "t": 192.64770999999996, "r": 238.32117, "b": 201.86072000000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79987, "t": 214.60748, "r": 546.4657, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9872201681137085, "cells": [{"id": 9, "text": "This paper is intended for database engineers, data-centric application developers, and ", "bbox": {"l": 136.79987, "t": 214.60748, "r": 524.18518, "b": 223.82050000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security officers who want to design and implement RCAC as a part of their data control and ", "bbox": {"l": 136.79987, "t": 226.6073, "r": 546.4657, "b": 235.82030999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "bbox": {"l": 136.79987, "t": 238.60712, "r": 521.25488, "b": 247.82012999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "database concepts, and SQL is assumed.", "bbox": {"l": 136.79987, "t": 250.60693000000003, "r": 321.69434, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"label": "section_header", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}, "confidence": 0.9255505204200745, "cells": [{"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Authors"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 341.84152, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318943977356, "cells": [{"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"label": "picture", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 142.52883911132812, "t": 375.0449523925781, "r": 251.47850036621094, "b": 503.20648193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9862572550773621, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 263.39957, "t": 375.64877, "r": 541.25079, "b": 516.85974, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "bbox": {"l": 263.3996, "t": 459.64749, "r": 528.91016, "b": 468.86047, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "numerous technical roles, including independent software ", "bbox": {"l": 263.3996, "t": 471.64731, "r": 520.24207, "b": 480.86029, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "vendors technical support on a broad range of IBM ", "bbox": {"l": 263.3996, "t": 483.64713, "r": 490.6967200000001, "b": 492.86011, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "technologies and products, and supporting customers in the ", "bbox": {"l": 263.3996, "t": 495.64694, "r": 530.95514, "b": 504.85992, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "IBM Executive Briefing Center and IBM Project Office.", "bbox": {"l": 263.3996, "t": 507.64676, "r": 501.62973, "b": 516.85974, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"label": "picture", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 145.41445922851562, "t": 527.2447509765625, "r": 252.08840942382812, "b": 635.383056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.987165629863739, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 263.3996, "t": 527.62653, "r": 541.27374, "b": 680.83716, "coord_origin": "TOPLEFT"}, "confidence": 0.9823779463768005, "cells": [{"id": 29, "text": "Hernando Bedoya", "bbox": {"l": 263.3996, "t": 527.62653, "r": 348.38229, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": " is a Senior IT Specialist at STG Lab ", "bbox": {"l": 348.41916, "t": 527.62653, "r": 512.3429, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Services and Training in Rochester, Minnesota. He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. His areas of expertise are database technology, ", "bbox": {"l": 263.3996, "t": 647.62454, "r": 524.77386, "b": 656.83754, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "performance, and data warehousing. Hernando can be ", "bbox": {"l": 263.3996, "t": 659.62434, "r": 508.27124, "b": 668.83735, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "contacted at ", "bbox": {"l": 263.3996, "t": 671.62415, "r": 320.63568, "b": 680.83716, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "hbedoya@us.ibm.com", "bbox": {"l": 320.63971, "t": 671.77356, "r": 410.57852, "b": 680.54832, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": ".", "bbox": {"l": 410.5795, "t": 671.62415, "r": 413.34839, "b": 680.83716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master\u2019s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}], "headers": [{"label": "page_footer", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9040942788124084, "cells": [{"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "xi"}, {"label": "page_footer", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469243884086609, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "1", "bbox": {"l": 541.67987, "t": 754.848721, "r": 547.21765, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Chapter 1.", "bbox": {"l": 81.0, "t": 268.54272000000003, "r": 115.13253, "b": 274.98071000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Securing and protecting IBM DB2 ", "bbox": {"l": 136.8, "t": 254.88635, "r": 547.30475, "b": 278.91785000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "data", "bbox": {"l": 136.8, "t": 285.84671, "r": 190.29802, "b": 309.8782, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "bbox": {"l": 136.8, "t": 348.70871, "r": 542.25665, "b": 357.92169, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 ", "bbox": {"l": 136.80096, "t": 360.70853, "r": 544.96643, "b": 369.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "bbox": {"l": 136.79965, "t": 372.70853, "r": 529.53839, "b": 381.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ ", "bbox": {"l": 136.79965, "t": 384.7083400000001, "r": 535.32874, "b": 393.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "bbox": {"l": 136.80026, "t": 396.70853, "r": 521.64374, "b": 405.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "bbox": {"l": 136.80026, "t": 408.7083400000001, "r": 547.13135, "b": 417.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "bbox": {"l": 136.80023, "t": 420.70816, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 525.06482, "b": 451.9407, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "bbox": {"l": 136.80025, "t": 454.72754000000003, "r": 518.26825, "b": 463.94052, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "industries, securing the data is required by law and subject to audits. 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All rights reserved."}], "body": [{"label": "picture", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "picture", "bbox": {"l": 32.05510711669922, "t": 70.42633819580078, "r": 239.62696838378906, "b": 238.0409698486328, "coord_origin": "TOPLEFT"}, "confidence": 0.7604207992553711, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 500.39999, "t": 93.16870000000006, "r": 522.61774, "b": 130.13171, "coord_origin": "TOPLEFT"}, "confidence": 0.7054201364517212, "cells": [{"id": 24, "text": "1", "bbox": {"l": 500.39999, "t": 93.16870000000006, "r": 522.61774, "b": 130.13171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}, {"label": "section_header", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 136.8, "t": 254.88635, "r": 547.30475, "b": 309.8782, "coord_origin": "TOPLEFT"}, "confidence": 0.9377050399780273, "cells": [{"id": 3, "text": "Securing and protecting IBM DB2 ", "bbox": {"l": 136.8, "t": 254.88635, "r": 547.30475, "b": 278.91785000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "data", "bbox": {"l": 136.8, "t": 285.84671, "r": 190.29802, "b": 309.8782, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Securing and protecting IBM DB2 data"}, {"label": "text", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "text", "bbox": {"l": 81.0, "t": 268.54272000000003, "r": 115.13253, "b": 274.98071000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "Chapter 1.", "bbox": {"l": 81.0, "t": 268.54272000000003, "r": 115.13253, "b": 274.98071000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 1."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79965, "t": 348.70871, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}, "confidence": 0.9868757724761963, "cells": [{"id": 5, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "bbox": {"l": 136.8, "t": 348.70871, "r": 542.25665, "b": 357.92169, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 ", "bbox": {"l": 136.80096, "t": 360.70853, "r": 544.96643, "b": 369.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "bbox": {"l": 136.79965, "t": 372.70853, "r": 529.53839, "b": 381.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ ", "bbox": {"l": 136.79965, "t": 384.7083400000001, "r": 535.32874, "b": 393.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "bbox": {"l": 136.80026, "t": 396.70853, "r": 521.64374, "b": 405.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "bbox": {"l": 136.80026, "t": 408.7083400000001, "r": 547.13135, "b": 417.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "bbox": {"l": 136.80023, "t": 420.70816, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 527.2063, "b": 487.94016, "coord_origin": "TOPLEFT"}, "confidence": 0.9865864515304565, "cells": [{"id": 12, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 525.06482, "b": 451.9407, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "bbox": {"l": 136.80025, "t": 454.72754000000003, "r": 518.26825, "b": 463.94052, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "bbox": {"l": 136.80025, "t": 466.72736, "r": 527.2063, "b": 475.94034, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "longer an option; it is a requirement.", "bbox": {"l": 136.80025, "t": 478.72717, "r": 296.31067, "b": 487.94016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80025, "t": 500.68698, "r": 547.15515, "b": 521.89978, "coord_origin": "TOPLEFT"}, "confidence": 0.9734498858451843, "cells": [{"id": 16, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "bbox": {"l": 136.80025, "t": 500.68698, "r": 547.15515, "b": 509.89996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "are covered in this chapter:", "bbox": {"l": 136.80025, "t": 512.6868, "r": 257.28036, "b": 521.89978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This chapter describes how you can secure and protect data in DB2 for i. 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. 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", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837489128112793, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621999263763428, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. 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Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> First, and most important, is the definition of a company\u2019s security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077319264411926, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. 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Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837489128112793, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621999263763428, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company\u2019s most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today\u2019s connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}, {"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889443874359131, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "section_header", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1 Security fundamentals"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9663435220718384, "cells": [{"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"label": "list_item", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.12487999999996, "r": 547.16425, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> First, and most important, is the definition of a company\u2019s security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077319264411926, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837489128112793, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621999263763428, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company\u2019s most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today\u2019s connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}], "headers": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889443874359131, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. 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Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858148097991943, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.9757603406906128, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838991165161133, "cells": [], "children": [{"id": 9, "label": "text", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.949161946773529, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. 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However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.9757603406906128, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"label": "picture", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838991165161133, "cells": [], "children": [{"id": 9, "label": "text", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1-2 Existing row and column controls"}, {"label": "page_footer", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_footer", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.949161946773529, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. 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Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"label": "section_header", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.3.1 Existing row and column control"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858148097991943, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. 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However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.9757603406906128, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], 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"cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_footer", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.949161946773529, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "2.2", "bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "VARCHAR(30)", "bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "DENIED: The user profile is not allowed to use the function.", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "USER_TYPE", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "VARCHAR(5)", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Type of user profile:", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "GROUP: The user profile is a group.", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.969738245010376, "cells": [{"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}, "confidence": 0.9551491737365723, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410472512245178, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417492747306824, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.95621657371521, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682287573814392, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056942462921143, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636377096176147, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737266302108765, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197836518287659, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 142.79999, "t": 296.5379899999999, "r": 539.10712, "b": 435.84369, "coord_origin": "TOPLEFT"}, "confidence": 0.9851696491241455, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "VARCHAR(30)", "bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 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"TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417492747306824, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.95621657371521, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682287573814392, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056942462921143, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", 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Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197836518287659, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 142.79999, "t": 296.5379899999999, "r": 539.10712, "b": 435.84369, "coord_origin": "TOPLEFT"}, "confidence": 0.9851696491241455, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, 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"confidence": 0.0, "cells": [{"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 49, "label": "text", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 52, "label": "text", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 71, "text": "GROUP: The user profile is a group.", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 51, "label": "text", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 70, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "ched", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl"], "num_rows": 5, "num_cols": 3, "table_cells": [{"bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Column name", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Data type", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Description", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "FUNCTION_ID", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(30)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "ID of the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_NAME", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(10)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 353.88333, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Name of the user profile that has a usage setting for this  function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USAGE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(7)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.41626, "t": 364.51862, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Usage setting: GLYPH<SM590000> ALLOWED: The user profile is allowed to use the function. GLYPH<SM590000> DENIED: The user profile is not allowed to use the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_TYPE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(5)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.43161, "t": 405.55865, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Type of user profile: GLYPH<SM590000> USER: The user profile is a user. GLYPH<SM590000> GROUP: The user profile is a group.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647642970085144, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8165044188499451, "cells": [{"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.65802, "r": 331.67731, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5808849930763245, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 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"coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 0.5631598830223083, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 32, "text": 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620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.54872, "r": 547.22345, "b": 709.760956, "coord_origin": "TOPLEFT"}, "confidence": 0.9853105545043945, "cells": [{"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9166075587272644, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9529877305030823, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.969738245010376, "cells": [{"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.6 Change Function Usage CL command"}, {"label": "text", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}, "confidence": 0.9551491737365723, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410472512245178, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417492747306824, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.95621657371521, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682287573814392, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056942462921143, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636377096176147, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737266302108765, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197836518287659, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 142.79999, "t": 296.5379899999999, "r": 539.10712, "b": 435.84369, "coord_origin": "TOPLEFT"}, "confidence": 0.9851696491241455, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, 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334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "DENIED: The user profile is not allowed to use the function.", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "USER_TYPE", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "VARCHAR(5)", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Type of user profile:", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": 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315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 36, "label": "text", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 40, "label": "text", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 45, "label": "text", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 64, "text": "DENIED: The user profile is not allowed to use the function.", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 63, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 46, "label": "text", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 65, "text": "USER_TYPE", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 47, "label": "text", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 66, "text": "VARCHAR(5)", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 48, "label": "text", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 67, "text": "Type of user profile:", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 50, "label": "text", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 49, "label": "text", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 52, "label": "text", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 71, "text": "GROUP: The user profile is a group.", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 51, "label": "text", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 70, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "ched", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl"], "num_rows": 5, "num_cols": 3, "table_cells": [{"bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Column name", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Data type", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Description", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "FUNCTION_ID", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(30)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "ID of the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_NAME", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(10)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 353.88333, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Name of the user profile that has a usage setting for this  function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USAGE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(7)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.41626, "t": 364.51862, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Usage setting: GLYPH<SM590000> ALLOWED: The user profile is allowed to use the function. GLYPH<SM590000> DENIED: The user profile is not allowed to use the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_TYPE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(5)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.43161, "t": 405.55865, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Type of user profile: GLYPH<SM590000> USER: The user profile is a user. GLYPH<SM590000> GROUP: The user profile is a group.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647642970085144, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8165044188499451, "cells": [{"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.65802, "r": 331.67731, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5808849930763245, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 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620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.54872, "r": 547.22345, "b": 709.760956, "coord_origin": "TOPLEFT"}, "confidence": 0.9853105545043945, "cells": [{"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}], "headers": [{"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9166075587272644, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9529877305030823, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 2. Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "11", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "bbox": {"l": 64.800003, "t": 383.5379899999999, "r": 391.75464, "b": 391.86301, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "User action", "bbox": {"l": 70.800301, "t": 400.51827999999995, "r": 119.78551, "b": 408.84329, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*JOBCTL", "bbox": {"l": 424.93805, "t": 447.52255, "r": 433.26297000000005, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "QIBM_DB_SECADM", "bbox": {"l": 450.13806, "t": 401.6000700000001, "r": 458.46298, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "QIBM_DB_SQLADM", "bbox": {"l": 475.93835000000007, "t": 401.53442, "r": 484.26327999999995, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "QIBM_DB_SYSMON", "bbox": {"l": 501.13837, "t": 401.6145, "r": 509.46329, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "No Authority", "bbox": {"l": 526.39862, "t": 432.79944, "r": 534.72357, "b": 487.02005, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "SET CURRENT DEGREE", "bbox": {"l": 70.800003, "t": 498.69299, "r": 151.6794, "b": 506.66699, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " (SQL statement)", "bbox": {"l": 151.6803, "t": 498.55798, "r": 220.15681000000004, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "X", "bbox": {"l": 429.0, "t": 498.55798, "r": 435.00299000000007, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 480.00031, "t": 498.55798, "r": 486.0033, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "CHGQRYA", "bbox": {"l": 70.800018, "t": 517.65329, "r": 102.23972, "b": 525.62729, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": " command targeting a different user\u2019s job", "bbox": {"l": 102.23972, "t": 517.51828, "r": 264.5538, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "X", "bbox": {"l": 429.00003, "t": 517.51828, "r": 435.00302000000005, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "X", "bbox": {"l": 480.00034, "t": 517.51828, "r": 486.00333, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "STRDBMON", "bbox": {"l": 70.800049, "t": 536.67299, "r": 106.73975, "b": 544.64699, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": " or ", "bbox": {"l": 106.73975, "t": 536.5379800000001, "r": 119.77895, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "ENDDBMON", "bbox": {"l": 119.69975000000001, "t": 536.67299, "r": 155.69974, "b": 544.64699, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": " commands targeting a different user\u2019s job", "bbox": {"l": 155.69974, "t": 536.5379800000001, "r": 322.50574, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "X", "bbox": {"l": 429.00003, "t": 536.5379800000001, "r": 435.00302000000005, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "X", "bbox": {"l": 480.00034, "t": 536.5379800000001, "r": 486.00333, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "STRDBMON", "bbox": {"l": 70.800049, "t": 555.69269, "r": 106.73975, "b": 563.66669, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": " or ", "bbox": {"l": 106.73975, "t": 555.55768, "r": 119.77895, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ENDDBMON", "bbox": {"l": 119.69975000000001, "t": 555.69269, "r": 155.69974, "b": 563.66669, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": " commands targeting a job that matches the current user", "bbox": {"l": 155.69974, "t": 555.55768, "r": 381.02185, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "X", "bbox": {"l": 429.00003, "t": 555.55768, "r": 435.00302000000005, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "X", "bbox": {"l": 480.00034, "t": 555.55768, "r": 486.00333, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "X", "bbox": {"l": 505.26061999999996, "t": 555.55768, "r": 511.26361, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "X", "bbox": {"l": 530.76031, "t": 555.55768, "r": 536.76331, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "QUSRJOBI() API format 900 or System i Navigator\u2019s SQL Details for Job", "bbox": {"l": 70.800049, "t": 574.51797, "r": 359.51736, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "X", "bbox": {"l": 429.0000600000001, "t": 574.51797, "r": 435.00305000000003, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "X", "bbox": {"l": 480.00037, "t": 574.51797, "r": 486.00335999999993, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "X", "bbox": {"l": 505.2606799999999, "t": 574.51797, "r": 511.26367, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Visual Explain within Run SQL scripts", "bbox": {"l": 70.800079, "t": 593.5376699999999, "r": 220.75178999999997, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "X", "bbox": {"l": 429.0000600000001, "t": 593.5376699999999, "r": 435.00305000000003, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "X", "bbox": {"l": 480.00037, "t": 593.5376699999999, "r": 486.00335999999993, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "X", "bbox": {"l": 505.2606799999999, "t": 593.5376699999999, "r": 511.26367, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "X", "bbox": {"l": 530.76038, "t": 593.5376699999999, "r": 536.76337, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "Visual Explain outside of Run SQL scripts", "bbox": {"l": 70.800079, "t": 612.55737, "r": 236.6548, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "X", "bbox": {"l": 429.0000600000001, "t": 612.55737, "r": 435.00305000000003, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "X", "bbox": {"l": 480.00037, "t": 612.55737, "r": 486.00335999999993, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "ANALYZE PLAN CACHE procedure", "bbox": {"l": 70.800079, "t": 631.51767, "r": 213.12968, "b": 639.84268, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "X", "bbox": {"l": 429.0000600000001, "t": 631.51767, "r": 435.00305000000003, "b": 639.84268, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "X", "bbox": {"l": 480.00037, "t": 631.51767, "r": 486.00335999999993, "b": 639.84268, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "DUMP PLAN CACHE procedure", "bbox": {"l": 70.800079, "t": 650.53737, "r": 199.87808, "b": 658.86238, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "X", "bbox": {"l": 429.0000600000001, "t": 650.53737, "r": 435.00305000000003, "b": 658.86238, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "X", "bbox": {"l": 480.00037, "t": 650.53737, "r": 486.00335999999993, "b": 658.86238, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "MODIFY PLAN CACHE procedure", "bbox": {"l": 70.800079, "t": 669.55708, "r": 208.36777, "b": 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"coord_origin": "TOPLEFT"}}, {"id": 77, "text": "X", "bbox": {"l": 429.0000600000001, "t": 707.537071, "r": 435.00305000000003, "b": 715.862068, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "X", "bbox": {"l": 480.00037, "t": 707.537071, "r": 486.00335999999993, "b": 715.862068, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 2, "label": "text", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9789126515388489, "cells": [{"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665539860725403, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "caption", "bbox": {"l": 64.800003, "t": 383.5379899999999, "r": 391.75464, "b": 391.86301, "coord_origin": "TOPLEFT"}, "confidence": 0.9337190985679626, "cells": [{"id": 23, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "bbox": {"l": 64.800003, "t": 383.5379899999999, "r": 391.75464, "b": 391.86301, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 70.800003, "t": 400.51827999999995, "r": 536.76337, "b": 715.862068, "coord_origin": "TOPLEFT"}, "confidence": 0.9899572730064392, "cells": [{"id": 24, "text": "User action", "bbox": {"l": 70.800301, "t": 400.51827999999995, "r": 119.78551, "b": 408.84329, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*JOBCTL", "bbox": {"l": 424.93805, "t": 447.52255, "r": 433.26297000000005, "b": 487.01999, 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This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa\u2019s job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665539860725403, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 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Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 2. Roles and separation of duties"}], "body": [{"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9789126515388489, "cells": [{"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa\u2019s job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665539860725403, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 64.800003, "t": 383.5379899999999, "r": 391.75464, "b": 391.86301, "coord_origin": "TOPLEFT"}, "confidence": 0.9337190985679626, "cells": [{"id": 23, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "bbox": {"l": 64.800003, "t": 383.5379899999999, "r": 391.75464, "b": 391.86301, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority"}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 70.800003, "t": 400.51827999999995, "r": 536.76337, "b": 715.862068, "coord_origin": "TOPLEFT"}, "confidence": 0.9899572730064392, "cells": [{"id": 24, "text": "User action", "bbox": {"l": 70.800301, "t": 400.51827999999995, "r": 119.78551, "b": 408.84329, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*JOBCTL", "bbox": {"l": 424.93805, "t": 447.52255, "r": 433.26297000000005, "b": 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Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 2. Roles and separation of duties"}]}}, {"page_no": 9, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "15", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The SQL ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 179.58179, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "CREATE PERMISSION", "bbox": {"l": 179.57977, "t": 71.65845000000002, "r": 264.47879, "b": 80.48302999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " statement that is shown in Figure 3-1 is used to define and ", "bbox": {"l": 264.53955, "t": 71.50903000000005, "r": 528.73059, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "initially enable or disable the row access rules.", "bbox": {"l": 136.79956, "t": 83.50885000000017, "r": 341.71762, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Figure 3-1 CREATE PERMISSION SQL statement", "bbox": {"l": 136.8, "t": 414.138, "r": 341.97659, "b": 422.46301, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Column mask", "bbox": {"l": 136.8, "t": 439.94399999999996, "r": 215.37601, "b": 451.04401, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "A column mask is a database object that manifests a column value access control rule for a ", "bbox": {"l": 136.8, "t": 455.08871000000005, "r": 542.76648, "b": 464.3017, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "specific column in a specific table. 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For example, a teller can see only the last four digits of a tax ", "bbox": {"l": 136.8, "t": 479.08835, "r": 538.80927, "b": 488.30133, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "identification number.", "bbox": {"l": 136.8, "t": 491.08817, "r": 231.20888, "b": 500.30115, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "CREATE PERMISSION", "bbox": {"l": 148.1337, "t": 139.67969000000005, "r": 246.7961, "b": 149.50982999999997, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "<", "bbox": {"l": 251.86685, "t": 139.67969000000005, "r": 257.58578, "b": 149.49834999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "permission name", "bbox": {"l": 257.59152, "t": 139.67969000000005, "r": 336.99741, "b": 149.50982999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ">", "bbox": {"l": 337.01233, "t": 139.67969000000005, "r": 342.73126, "b": 149.49834999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Names the row permission for row access control", "bbox": {"l": 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For example, a teller can see only the last four digits of a tax identification number."}], "headers": [{"label": "page_footer", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9071078896522522, "cells": [{"id": 1, "text": "15", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "15"}, {"label": "page_footer", "id": 2, "page_no": 9, "cluster": {"id": 2, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9526246786117554, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}]}}, {"page_no": 10, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "P1", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Proc1:", "bbox": {"l": 148.4301, "t": 473.58524, "r": 174.05859, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Owner = JOE", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "SET OPTION USRPRF=*OWNER", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "USER = ALICE", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "CURRENT USER = JOE", "bbox": {"l": 148.4301, "t": 533.30984, "r": 234.57686999999999, "b": 541.82059, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 566.15842, "r": 191.70256, "b": 574.66917, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 578.10333, "r": 232.56117, "b": 586.61409, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "caption", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.7875164747238159, "cells": [{"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253707528114319, "cells": [{"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 535.65082, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 20, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445975422859192, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748277068138123, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 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"USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}, "confidence": 0.6404176950454712, "cells": [{"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}, "confidence": 0.5759296417236328, "cells": [{"id": 43, "text": "P1", 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When no adopted  authority is present, this has the same value as USER.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "SYSTEM_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The authorization ID that initiated the connection.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445975422859192, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE\u2019s authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748277068138123, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "picture", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 135.64837646484375, "t": 384.1736755371094, "r": 301.2367248535156, "b": 594.7566528320312, "coord_origin": "TOPLEFT"}, "confidence": 0.7221462726593018, "cells": [], "children": [{"id": 15, "label": "text", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}, "confidence": 0.7616674900054932, "cells": [{"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 40, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}, "confidence": 0.6404176950454712, "cells": [{"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}, "confidence": 0.5759296417236328, "cells": [{"id": 43, "text": "P1", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Proc1:", "bbox": {"l": 148.4301, "t": 473.58524, "r": 174.05859, "b": 482.09598, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 45, "text": "Owner = JOE", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 46, "text": "SET OPTION USRPRF=*OWNER", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": 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[{"id": 50, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 578.10333, "r": 232.56117, "b": 586.61409, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274529814720154, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"id": 21, 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global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9130509495735168, "cells": [{"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "19"}, {"label": "page_footer", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9557498693466187, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}], "body": [{"label": "caption", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.7875164747238159, "cells": [{"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-1 summarizes these special registers and their values."}, {"label": "caption", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253707528114319, "cells": [{"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-1 Special registers and their corresponding values"}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 535.65082, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 20, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl"], "num_rows": 4, "num_cols": 2, "table_cells": [{"bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Special register", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Corresponding value", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER or SESSION_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread excluding adopted authority.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "CURRENT_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread including adopted authority. When no adopted  authority is present, this has the same value as USER.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "SYSTEM_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The authorization ID that initiated the connection.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445975422859192, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE\u2019s authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748277068138123, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "picture", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 135.64837646484375, "t": 384.1736755371094, "r": 301.2367248535156, "b": 594.7566528320312, "coord_origin": "TOPLEFT"}, "confidence": 0.7221462726593018, "cells": [], "children": [{"id": 15, "label": "text", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}, "confidence": 0.7616674900054932, "cells": [{"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 40, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}, "confidence": 0.6404176950454712, "cells": [{"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}, "confidence": 0.5759296417236328, "cells": [{"id": 43, "text": "P1", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Proc1:", "bbox": {"l": 148.4301, "t": 473.58524, "r": 174.05859, "b": 482.09598, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 45, "text": "Owner = JOE", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 46, "text": "SET OPTION USRPRF=*OWNER", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 47, "text": "USER = ALICE", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 148.4301, "t": 533.30984, "r": 234.57686999999999, "b": 541.82059, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 48, "text": "CURRENT USER = JOE", "bbox": {"l": 148.4301, "t": 533.30984, "r": 234.57686999999999, "b": 541.82059, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 36, "label": "text", "bbox": {"l": 138.476, "t": 566.15842, "r": 191.70256, "b": 574.66917, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 49, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 566.15842, "r": 191.70256, "b": 574.66917, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 138.476, "t": 578.10333, "r": 232.56117, "b": 586.61409, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 50, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 578.10333, "r": 232.56117, "b": 586.61409, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274529814720154, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.2.2 Built-in global variables"}, {"label": "text", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 672.92153, "coord_origin": "TOPLEFT"}, "confidence": 0.9696778059005737, "cells": [{"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}], "headers": [{"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9130509495735168, "cells": [{"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "19"}, {"label": "page_footer", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9557498693466187, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Description", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CLIENT_HOST", "bbox": {"l": 70.800003, "t": 129.49834999999996, "r": 132.7209, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "VARCHAR(255)", "bbox": {"l": 202.89029, "t": 129.49834999999996, "r": 267.07651, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Host name of the current client as returned by the system", "bbox": {"l": 281.84732, "t": 129.49834999999996, "r": 510.17548, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "CLIENT_IPADDR", "bbox": {"l": 70.800018, "t": 148.51806999999997, "r": 140.66522, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "VARCHAR(128)", "bbox": {"l": 202.87231, "t": 148.51806999999997, "r": 267.07739, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "IP address of the current client as returned by the system", "bbox": {"l": 281.84549, "t": 148.51806999999997, "r": 509.60583, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "CLIENT_PORT ", "bbox": {"l": 70.800018, "t": 167.53778, "r": 134.98264, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "INTEGER", "bbox": {"l": 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These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. 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It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237534403800964, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338628649711609, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514462947845459, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512302875518799, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.706649661064148, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The 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"start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Type of the currently running routine", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.52872, "r": 547.23474, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237534403800964, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338628649711609, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514462947845459, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512302875518799, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.706649661064148, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"label": "page_footer", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9187921285629272, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "20"}, {"label": "page_footer", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9505080580711365, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8370980620384216, "cells": [{"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 lists the nine built-in global variables."}, {"label": "caption", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9132355451583862, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 527.59222, "b": 289.86227, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 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"coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "ROUTINE_SCHEMA", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 202.79312, "t": 243.55724999999995, "r": 267.09274, "b": 251.8822, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(128)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 281.87164, "t": 243.55724999999995, "r": 464.26022, "b": 251.8822, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Schema name of the currently running routine", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 70.800018, "t": 262.51757999999995, "r": 188.43991, "b": 270.84253, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "ROUTINE_SPECIFIC_NAME", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 202.84441, "t": 262.51757999999995, "r": 267.03693, "b": 270.84253, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(128)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 281.80682, "t": 262.51757999999995, "r": 430.40045, "b": 270.84253, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Name of the currently running routine", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 70.800034, "t": 281.53726, "r": 139.43135, "b": 289.86227, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "ROUTINE_TYPE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 202.74635, "t": 281.53726, "r": 239.28996000000004, "b": 289.86227, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "CHAR(1)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 281.79065, "t": 281.53726, "r": 425.09131, "b": 289.86227, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Type of the currently running routine", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.52872, "r": 547.23474, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237534403800964, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338628649711609, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514462947845459, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512302875518799, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.706649661064148, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}], "headers": [{"label": "page_footer", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9187921285629272, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "20"}, {"label": "page_footer", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9505080580711365, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606333613395691, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897271037101746, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785220146179199, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678944706916809, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467734694480896, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705167412757874, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469641447067261, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003496885299683, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.848820149898529, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411225080490112, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.910578727722168, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606333613395691, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897271037101746, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785220146179199, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678944706916809, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467734694480896, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705167412757874, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469641447067261, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003496885299683, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.848820149898529, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411225080490112, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.910578727722168, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "27"}, {"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}], "body": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606333613395691, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897271037101746, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785220146179199, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678944706916809, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467734694480896, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705167412757874, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469641447067261, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003496885299683, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.848820149898529, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411225080490112, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}], "headers": [{"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.910578727722168, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "27"}, {"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}]}}, {"page_no": 13, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 63.80192184448242, "t": 95.38238525390625, "r": 547.11474609375, "b": 170.0321044921875, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570101976394653, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.600240170955658, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985856056213379, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449256062507629, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.9049987792969, "r": 530.0478515625, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801458716392517, "cells": [], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.80192184448242, "t": 95.38238525390625, "r": 547.11474609375, "b": 170.0321044921875, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570101976394653, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.600240170955658, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985856056213379, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Column Access Control (masks)"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "*/"}, {"label": "text", "id": 16, "page_no": 13, "cluster": {"id": 16, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 17, "page_no": 13, "cluster": {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 18, "page_no": 13, "cluster": {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449256062507629, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.9049987792969, "r": 530.0478515625, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801458716392517, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.80192184448242, "t": 95.38238525390625, "r": 547.11474609375, "b": 170.0321044921875, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570101976394653, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.600240170955658, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985856056213379, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Column Access Control (masks)"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "*/"}, {"label": "text", "id": 16, "page_no": 13, "cluster": {"id": 16, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 17, "page_no": 13, "cluster": {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 18, "page_no": 13, "cluster": {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449256062507629, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.9049987792969, "r": 530.0478515625, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801458716392517, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}], "headers": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 14, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 4. 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Implementing Row and Column Access Control: Banking example ", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}]}}, {"page_no": 15, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632163763046265, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8994000554084778, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632163763046265, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}, {"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8994000554084778, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "124"}, {"label": "page_footer", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632163763046265, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}], "headers": [{"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8994000554084778, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "124"}, {"label": "page_footer", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 16, "size": {"width": 612.0, "height": 792.0}, "cells": [], "predictions": {"layout": {"clusters": [{"id": 0, "label": "form", "bbox": {"l": 0.4932013750076294, "t": 0.0, "r": 610.2305297851562, "b": 791.654541015625, "coord_origin": "TOPLEFT"}, "confidence": 0.6497195363044739, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "form", "id": 0, "page_no": 16, "cluster": {"id": 0, "label": "form", "bbox": {"l": 0.4932013750076294, "t": 0.0, "r": 610.2305297851562, "b": 791.654541015625, "coord_origin": "TOPLEFT"}, "confidence": 0.6497195363044739, "cells": [], "children": []}, "text": null}], "body": [{"label": "form", "id": 0, "page_no": 16, "cluster": {"id": 0, "label": "form", "bbox": {"l": 0.4932013750076294, "t": 0.0, "r": 610.2305297851562, "b": 791.654541015625, "coord_origin": "TOPLEFT"}, "confidence": 0.6497195363044739, "cells": [], "children": []}, "text": null}], "headers": []}}, {"page_no": 17, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "fi", "bbox": {"l": 558.11987, "t": 45.468689999999924, "r": 565.46039, "b": 54.68169999999998, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "REDP-5110-00", "bbox": {"l": 171.0, "t": 631.338, "r": 231.88769999999997, "b": 639.66301, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "INTERNATIONAL ", "bbox": {"l": 467.3399999999999, "t": 247.71831999999995, "r": 559.80933, "b": 260.16052, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "TECHNICAL", "bbox": {"l": 467.3399999999999, "t": 261.75842, "r": 529.50208, "b": 274.20061999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "SUPPORT", "bbox": {"l": 467.3399999999999, "t": 275.73839999999996, "r": 518.93317, "b": 288.1806, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ORGANIZATION", "bbox": {"l": 467.3399999999999, "t": 289.71841, "r": 550.7475, "b": 302.16061, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "BUILDING TECHNICAL ", "bbox": {"l": 467.3399999999999, "t": 351.79199, "r": 571.70758, "b": 362.47198, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "INFORMATION BASED ON ", "bbox": {"l": 467.3399999999999, "t": 363.79199, "r": 587.38916, "b": 374.47198, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "PRACTICAL EXPERIENCE", "bbox": {"l": 467.3399999999999, "t": 375.79199, "r": 582.5556, "b": 386.47198, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "IBM Redbooks are developed ", "bbox": {"l": 467.3399999999999, "t": 399.8602900000001, "r": 587.46674, "b": 409.63251, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "by the IBM International ", "bbox": {"l": 467.3399999999999, "t": 410.90067, "r": 566.34229, "b": 420.67285, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Technical Support ", "bbox": {"l": 467.3399999999999, "t": 421.88068, "r": 543.20404, "b": 431.65289, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Organization. Experts from ", "bbox": {"l": 467.3399999999999, "t": 432.8606899999999, "r": 577.76697, "b": 442.63287, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "IBM, Customers and Partners ", "bbox": {"l": 467.3399999999999, "t": 443.90106, "r": 587.40948, "b": 453.67328, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "from around the world create ", "bbox": {"l": 467.3399999999999, "t": 454.88107, "r": 587.52051, "b": 464.65326, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "timely technical information ", "bbox": {"l": 467.3399999999999, "t": 465.86108, "r": 582.67505, "b": 475.6333, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "based on realistic scenarios. ", "bbox": {"l": 467.3399999999999, "t": 476.90146, "r": 585.46722, "b": 486.67365, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Specific recommendations ", "bbox": {"l": 467.3399999999999, "t": 487.88147, "r": 577.70874, "b": 497.65369, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "are provided to help you ", "bbox": {"l": 467.3399999999999, "t": 498.86148, "r": 568.03546, "b": 508.63367, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implement IT solutions more ", "bbox": {"l": 467.3399999999999, "t": 509.90186, "r": 585.44525, "b": 519.67407, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "effectively in your ", "bbox": {"l": 467.3399999999999, "t": 520.8818699999999, "r": 541.4967, "b": 530.65405, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "environment.", "bbox": {"l": 467.3399999999999, "t": 531.8618799999999, "r": 520.64893, "b": 541.63406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "For more information:", "bbox": {"l": 467.3399999999999, "t": 578.83191, "r": 570.948, "b": 589.5119, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ibm.com", "bbox": {"l": 467.3399999999999, "t": 590.83191, "r": 508.59961, "b": 601.5119, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "/redbooks", "bbox": {"l": 508.56000000000006, "t": 590.83191, "r": 552.74518, "b": 601.5119, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Redpaper", "bbox": {"l": 474.60001, "t": 164.05658000000005, "r": 580.88989, "b": 188.94097999999997, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "\u2122", "bbox": {"l": 582.53992, "t": 172.32714999999996, "r": 592.13989, "b": 181.20714999999996, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Row and Column Access Control ", "bbox": {"l": 27.0, "t": 73.63799999999992, "r": 447.36002, "b": 103.00800000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Support in IBM DB2 for i", "bbox": {"l": 27.0, "t": 113.76000999999997, "r": 314.43002, "b": 140.46002, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Implement roles and ", "bbox": {"l": 26.700001, "t": 242.17200000000003, "r": 127.4436, "b": 252.85199, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "separation of duties", "bbox": {"l": 26.700001, "t": 256.15198, "r": 121.6608, "b": 266.83196999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Leverage row ", "bbox": {"l": 26.700001, "t": 284.17197, "r": 93.970795, "b": 294.85196, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "permissions on the ", "bbox": {"l": 26.700001, "t": 298.15198000000004, "r": 120.28319999999998, "b": 308.83197, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "database", "bbox": {"l": 26.700001, "t": 312.19199000000003, "r": 70.413605, "b": 322.87198, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Protect columns by ", "bbox": {"l": 26.700001, "t": 340.15198000000004, "r": 121.44960000000002, "b": 350.83197, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "defining column ", "bbox": {"l": 26.700001, "t": 354.19199000000003, "r": 106.5696, "b": 364.87198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "masks", "bbox": {"l": 26.700001, "t": 368.1720000000001, "r": 58.194, "b": 378.85199, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "This IBM Redpaper publication provides information about the IBM i 7.2 ", "bbox": {"l": 152.94, "t": 242.72857999999997, "r": 413.99057, "b": 251.59295999999995, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "feature of IBM DB2 for i Row and Column Access Control (RCAC). 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2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3.6.2", "bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 173.32271, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8541, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "23", "bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "3.6.3", "bbox": {"l": 151.19717, "t": 643.66034, "r": 173.32227, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.85353, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "24", "bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "3.6.4", "bbox": {"l": 151.19717, "t": 656.14021, "r": 173.35289, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89182, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "25", "bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "3.6.5", "bbox": {"l": 151.19717, "t": 668.62009, "r": 173.35289, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Defining and creating column masks", "bbox": {"l": 178.89182, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.98996, "t": 668.62009, "r": 530.54413, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "26", "bbox": {"l": 536.08301, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "3.6.6", "bbox": {"l": 151.19717, "t": 681.15973, "r": 173.38359, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.93019, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "28", "bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3.6.7", "bbox": {"l": 151.19717, "t": 693.63961, "r": 173.32332, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Demonstrating data access with RCAC", "bbox": {"l": 178.85486, "t": 693.63961, "r": 350.80011, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 356.33163, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "29", "bbox": {"l": 536.10663, "t": 693.63961, "r": 547.16968, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "3.6.8", "bbox": {"l": 151.19717, "t": 706.119492, "r": 173.44592, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00812, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "32", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "document_index", "bbox": {"l": 136.15110778808594, "t": 132.0302734375, "r": 547.5270385742188, "b": 715.6527709960938, "coord_origin": "TOPLEFT"}, "confidence": 0.9803490042686462, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3.6.2", "bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 173.32271, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8541, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "23", "bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "3.6.3", "bbox": {"l": 151.19717, "t": 643.66034, "r": 173.32227, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.85353, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "24", "bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "3.6.4", "bbox": {"l": 151.19717, "t": 656.14021, "r": 173.35289, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89182, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "25", "bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "3.6.5", "bbox": {"l": 151.19717, "t": 668.62009, "r": 173.35289, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Defining and creating column masks", "bbox": {"l": 178.89182, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.98996, "t": 668.62009, "r": 530.54413, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "26", "bbox": {"l": 536.08301, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "3.6.6", "bbox": {"l": 151.19717, "t": 681.15973, "r": 173.38359, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.93019, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "28", "bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3.6.7", "bbox": {"l": 151.19717, "t": 693.63961, "r": 173.32332, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Demonstrating data access with RCAC", "bbox": {"l": 178.85486, "t": 693.63961, "r": 350.80011, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 356.33163, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "29", "bbox": {"l": 536.10663, "t": 693.63961, "r": 547.16968, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "3.6.8", "bbox": {"l": 151.19717, "t": 706.119492, "r": 173.44592, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00812, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "32", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "children": [{"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 195.39685, "t": 145.12847999999997, 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with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00812, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 128, "label": "text", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 127, "text": "32", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 3, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.892595112323761, "cells": [{"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9473134279251099, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.15110778808594, "t": 132.0302734375, "r": 547.5270385742188, "b": 715.6527709960938, "coord_origin": "TOPLEFT"}, "confidence": 0.9803490042686462, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 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. . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 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built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 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"row_section": false}, {"bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, 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1, "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0882, "t": 643.66034, "r": 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"row_section": false}, {"bbox": {"l": 151.19717, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.5 Defining and creating column masks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.98996, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 0, 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false, "row_section": false}]}}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Contents"}, {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.15110778808594, "t": 132.0302734375, "r": 547.5270385742188, "b": 715.6527709960938, "coord_origin": "TOPLEFT"}, "confidence": 0.9803490042686462, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3.6.2", "bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 173.32271, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8541, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "23", "bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "3.6.3", "bbox": {"l": 151.19717, "t": 643.66034, "r": 173.32227, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.85353, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "24", 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"b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 30, "end_row_offset_idx": 31, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 30, "end_row_offset_idx": 31, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "19", "column_header": false, "row_header": false, 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"end_col_offset_idx": 1, "text": "3.4 Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 32, "end_row_offset_idx": 33, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "21", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79701, "t": 593.62131, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC 3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, 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1, "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "24", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "25", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.5 Defining and creating column masks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.98996, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "28", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10663, "t": 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false, "row_section": false}]}, {"label": "page_footer", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.892595112323761, "cells": [{"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "iii"}, {"label": "page_footer", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9473134279251099, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}], "body": [{"label": "section_header", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Contents"}, {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.15110778808594, "t": 132.0302734375, "r": 547.5270385742188, "b": 715.6527709960938, "coord_origin": "TOPLEFT"}, "confidence": 0.9803490042686462, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 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. . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Preface", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 542.91888, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "DB2fi for i Row and Column Access Control (RCAC). 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A solid background in IBM i object level security, DB2 for i relational ", "bbox": {"l": 136.79987, "t": 238.60712, "r": 521.25488, "b": 247.82012999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "database concepts, and SQL is assumed.", "bbox": {"l": 136.79987, "t": 250.60693000000003, "r": 321.69434, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. 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He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. 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His areas of expertise are database technology, ", "bbox": {"l": 263.3996, "t": 647.62454, "r": 524.77386, "b": 656.83754, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "performance, and data warehousing. Hernando can be ", "bbox": {"l": 263.3996, "t": 659.62434, "r": 508.27124, "b": 668.83735, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "contacted at ", "bbox": {"l": 263.3996, "t": 671.62415, "r": 320.63568, "b": 680.83716, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "hbedoya@us.ibm.com", "bbox": {"l": 320.63971, "t": 671.77356, "r": 410.57852, "b": 680.54832, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": ".", "bbox": {"l": 410.5795, "t": 671.62415, "r": 413.34839, "b": 680.83716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master\u2019s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"label": "page_footer", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9040942788124084, "cells": [{"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "xi"}, {"label": "page_footer", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469243884086609, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}], "body": [{"label": "section_header", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9174709916114807, "cells": [{"id": 2, "text": "Preface", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preface"}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79984, "t": 132.64862000000005, "r": 547.30823, "b": 201.86072000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9869155883789062, "cells": [{"id": 3, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 542.91888, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "bbox": {"l": 136.79984, "t": 144.64844000000005, "r": 526.65509, "b": 153.86145, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "bbox": {"l": 136.79984, "t": 156.64824999999996, "r": 536.82135, "b": 165.86127, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "bbox": {"l": 136.79987, "t": 168.64806999999996, "r": 544.67975, "b": 177.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "of defining, creating, and implementing the row permissions and column masks in a relational ", "bbox": {"l": 136.79987, "t": 180.64788999999996, "r": 547.30823, "b": 189.86090000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "database environment.", "bbox": {"l": 136.79987, "t": 192.64770999999996, "r": 238.32117, "b": 201.86072000000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79987, "t": 214.60748, "r": 546.4657, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9872201681137085, "cells": [{"id": 9, "text": "This paper is intended for database engineers, data-centric application developers, and ", "bbox": {"l": 136.79987, "t": 214.60748, "r": 524.18518, "b": 223.82050000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security officers who want to design and implement RCAC as a part of their data control and ", "bbox": {"l": 136.79987, "t": 226.6073, "r": 546.4657, "b": 235.82030999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "bbox": {"l": 136.79987, "t": 238.60712, "r": 521.25488, "b": 247.82012999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "database concepts, and SQL is assumed.", "bbox": {"l": 136.79987, "t": 250.60693000000003, "r": 321.69434, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"label": "section_header", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}, "confidence": 0.9255505204200745, "cells": [{"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Authors"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 341.84152, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318943977356, "cells": [{"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"label": "picture", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 142.52883911132812, "t": 375.0449523925781, "r": 251.47850036621094, "b": 503.20648193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9862572550773621, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 263.39957, "t": 375.64877, "r": 541.25079, "b": 516.85974, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "bbox": {"l": 263.3996, "t": 459.64749, "r": 528.91016, "b": 468.86047, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "numerous technical roles, including independent software ", "bbox": {"l": 263.3996, "t": 471.64731, "r": 520.24207, "b": 480.86029, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "vendors technical support on a broad range of IBM ", "bbox": {"l": 263.3996, "t": 483.64713, "r": 490.6967200000001, "b": 492.86011, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "technologies and products, and supporting customers in the ", "bbox": {"l": 263.3996, "t": 495.64694, "r": 530.95514, "b": 504.85992, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "IBM Executive Briefing Center and IBM Project Office.", "bbox": {"l": 263.3996, "t": 507.64676, "r": 501.62973, "b": 516.85974, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"label": "picture", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 145.41445922851562, "t": 527.2447509765625, "r": 252.08840942382812, "b": 635.383056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.987165629863739, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 263.3996, "t": 527.62653, "r": 541.27374, "b": 680.83716, "coord_origin": "TOPLEFT"}, "confidence": 0.9823779463768005, "cells": [{"id": 29, "text": "Hernando Bedoya", "bbox": {"l": 263.3996, "t": 527.62653, "r": 348.38229, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": " is a Senior IT Specialist at STG Lab ", "bbox": {"l": 348.41916, "t": 527.62653, "r": 512.3429, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Services and Training in Rochester, Minnesota. He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. His areas of expertise are database technology, ", "bbox": {"l": 263.3996, "t": 647.62454, "r": 524.77386, "b": 656.83754, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "performance, and data warehousing. Hernando can be ", "bbox": {"l": 263.3996, "t": 659.62434, "r": 508.27124, "b": 668.83735, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "contacted at ", "bbox": {"l": 263.3996, "t": 671.62415, "r": 320.63568, "b": 680.83716, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "hbedoya@us.ibm.com", "bbox": {"l": 320.63971, "t": 671.77356, "r": 410.57852, "b": 680.54832, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": ".", "bbox": {"l": 410.5795, "t": 671.62415, "r": 413.34839, "b": 680.83716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master\u2019s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}], "headers": [{"label": "page_footer", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9040942788124084, "cells": [{"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "xi"}, {"label": "page_footer", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469243884086609, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "1", "bbox": {"l": 541.67987, "t": 754.848721, "r": 547.21765, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Chapter 1.", "bbox": {"l": 81.0, "t": 268.54272000000003, "r": 115.13253, "b": 274.98071000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Securing and protecting IBM DB2 ", "bbox": {"l": 136.8, "t": 254.88635, "r": 547.30475, "b": 278.91785000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "data", "bbox": {"l": 136.8, "t": 285.84671, "r": 190.29802, "b": 309.8782, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "bbox": {"l": 136.8, "t": 348.70871, "r": 542.25665, "b": 357.92169, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 ", "bbox": {"l": 136.80096, "t": 360.70853, "r": 544.96643, "b": 369.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "bbox": {"l": 136.79965, "t": 372.70853, "r": 529.53839, "b": 381.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ ", "bbox": {"l": 136.79965, "t": 384.7083400000001, "r": 535.32874, "b": 393.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "bbox": {"l": 136.80026, "t": 396.70853, "r": 521.64374, "b": 405.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "bbox": {"l": 136.80026, "t": 408.7083400000001, "r": 547.13135, "b": 417.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "bbox": {"l": 136.80023, "t": 420.70816, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 525.06482, "b": 451.9407, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "bbox": {"l": 136.80025, "t": 454.72754000000003, "r": 518.26825, "b": 463.94052, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "industries, securing the data is required by law and subject to audits. 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All rights reserved."}], "body": [{"label": "picture", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "picture", "bbox": {"l": 32.05510711669922, "t": 70.42633819580078, "r": 239.62696838378906, "b": 238.0409698486328, "coord_origin": "TOPLEFT"}, "confidence": 0.7604207992553711, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 500.39999, "t": 93.16870000000006, "r": 522.61774, "b": 130.13171, "coord_origin": "TOPLEFT"}, "confidence": 0.7054201364517212, "cells": [{"id": 24, "text": "1", "bbox": {"l": 500.39999, "t": 93.16870000000006, "r": 522.61774, "b": 130.13171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}, {"label": "section_header", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 136.8, "t": 254.88635, "r": 547.30475, "b": 309.8782, "coord_origin": "TOPLEFT"}, "confidence": 0.9377050399780273, "cells": [{"id": 3, "text": "Securing and protecting IBM DB2 ", "bbox": {"l": 136.8, "t": 254.88635, "r": 547.30475, "b": 278.91785000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "data", "bbox": {"l": 136.8, "t": 285.84671, "r": 190.29802, "b": 309.8782, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Securing and protecting IBM DB2 data"}, {"label": "text", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "text", "bbox": {"l": 81.0, "t": 268.54272000000003, "r": 115.13253, "b": 274.98071000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "Chapter 1.", "bbox": {"l": 81.0, "t": 268.54272000000003, "r": 115.13253, "b": 274.98071000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 1."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79965, "t": 348.70871, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}, "confidence": 0.9868757724761963, "cells": [{"id": 5, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "bbox": {"l": 136.8, "t": 348.70871, "r": 542.25665, "b": 357.92169, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 ", "bbox": {"l": 136.80096, "t": 360.70853, "r": 544.96643, "b": 369.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "bbox": {"l": 136.79965, "t": 372.70853, "r": 529.53839, "b": 381.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ ", "bbox": {"l": 136.79965, "t": 384.7083400000001, "r": 535.32874, "b": 393.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "bbox": {"l": 136.80026, "t": 396.70853, "r": 521.64374, "b": 405.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "bbox": {"l": 136.80026, "t": 408.7083400000001, "r": 547.13135, "b": 417.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "bbox": {"l": 136.80023, "t": 420.70816, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 527.2063, "b": 487.94016, "coord_origin": "TOPLEFT"}, "confidence": 0.9865864515304565, "cells": [{"id": 12, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 525.06482, "b": 451.9407, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "bbox": {"l": 136.80025, "t": 454.72754000000003, "r": 518.26825, "b": 463.94052, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "bbox": {"l": 136.80025, "t": 466.72736, "r": 527.2063, "b": 475.94034, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "longer an option; it is a requirement.", "bbox": {"l": 136.80025, "t": 478.72717, "r": 296.31067, "b": 487.94016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80025, "t": 500.68698, "r": 547.15515, "b": 521.89978, "coord_origin": "TOPLEFT"}, "confidence": 0.9734498858451843, "cells": [{"id": 16, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "bbox": {"l": 136.80025, "t": 500.68698, "r": 547.15515, "b": 509.89996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "are covered in this chapter:", "bbox": {"l": 136.80025, "t": 512.6868, "r": 257.28036, "b": 521.89978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This chapter describes how you can secure and protect data in DB2 for i. 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. 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", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837489128112793, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621999263763428, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. 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Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> First, and most important, is the definition of a company\u2019s security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077319264411926, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. 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Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837489128112793, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621999263763428, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company\u2019s most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today\u2019s connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}, {"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889443874359131, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "section_header", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1 Security fundamentals"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9663435220718384, "cells": [{"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"label": "list_item", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.12487999999996, "r": 547.16425, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> First, and most important, is the definition of a company\u2019s security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077319264411926, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837489128112793, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621999263763428, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company\u2019s most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today\u2019s connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}], "headers": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889443874359131, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. 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Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858148097991943, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.9757603406906128, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838991165161133, "cells": [], "children": [{"id": 9, "label": "text", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.949161946773529, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. 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However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.9757603406906128, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"label": "picture", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838991165161133, "cells": [], "children": [{"id": 9, "label": "text", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1-2 Existing row and column controls"}, {"label": "page_footer", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_footer", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.949161946773529, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. 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Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"label": "section_header", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.3.1 Existing row and column control"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858148097991943, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. 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However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.9757603406906128, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], 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"cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_footer", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.949161946773529, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "2.2", "bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "VARCHAR(30)", "bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "DENIED: The user profile is not allowed to use the function.", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "USER_TYPE", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "VARCHAR(5)", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Type of user profile:", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "GROUP: The user profile is a group.", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.969738245010376, "cells": [{"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}, "confidence": 0.9551491737365723, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410472512245178, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417492747306824, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.95621657371521, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682287573814392, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056942462921143, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636377096176147, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737266302108765, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197836518287659, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 135.5250701904297, "t": 289.7249450683594, "r": 545.87060546875, "b": 442.0505065917969, "coord_origin": "TOPLEFT"}, "confidence": 0.9851696491241455, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "VARCHAR(30)", "bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 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"b": 106.70172000000014, "coord_origin": "TOPLEFT"}, "confidence": 0.9551491737365723, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410472512245178, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417492747306824, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.95621657371521, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682287573814392, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056942462921143, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636377096176147, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737266302108765, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197836518287659, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.5250701904297, "t": 289.7249450683594, "r": 545.87060546875, "b": 442.0505065917969, "coord_origin": "TOPLEFT"}, "confidence": 0.9851696491241455, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, 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270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 45, "label": "text", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 64, "text": "DENIED: The user profile is not allowed to use the function.", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 63, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 46, "label": "text", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, 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GLYPH<SM590000> DENIED: The user profile is not allowed to use the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_TYPE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(5)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.43161, "t": 405.55865, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Type of user profile: GLYPH<SM590000> USER: The user profile is a user. GLYPH<SM590000> GROUP: The user profile is a group.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647642970085144, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 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Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9166075587272644, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9529877305030823, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.969738245010376, "cells": [{"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.6 Change Function Usage CL command"}, {"label": "text", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}, "confidence": 0.9551491737365723, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410472512245178, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417492747306824, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.95621657371521, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682287573814392, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056942462921143, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636377096176147, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737266302108765, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197836518287659, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.5250701904297, "t": 289.7249450683594, "r": 545.87060546875, "b": 442.0505065917969, "coord_origin": "TOPLEFT"}, "confidence": 0.9851696491241455, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, 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{"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": 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293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 45, "label": "text", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 64, "text": "DENIED: The user profile is not allowed to use the function.", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 63, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 46, "label": "text", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, 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2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(30)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "ID of the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_NAME", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(10)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 353.88333, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Name of the user profile that has a usage setting for this  function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USAGE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(7)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.41626, "t": 364.51862, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Usage setting: GLYPH<SM590000> ALLOWED: The user profile is allowed to use the function. GLYPH<SM590000> DENIED: The user profile is not allowed to use the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_TYPE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(5)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.43161, "t": 405.55865, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Type of user profile: GLYPH<SM590000> USER: The user profile is a user. GLYPH<SM590000> GROUP: The user profile is a group.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647642970085144, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 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178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 0.5631598830223083, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}, "confidence": 0.0, 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"bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.54872, "r": 547.22345, "b": 709.760956, "coord_origin": "TOPLEFT"}, "confidence": 0.9853105545043945, "cells": [{"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}], "headers": [{"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9166075587272644, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9529877305030823, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 2. Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "11", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "bbox": {"l": 64.800003, "t": 383.5379899999999, "r": 391.75464, "b": 391.86301, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "User action", "bbox": {"l": 70.800301, "t": 400.51827999999995, "r": 119.78551, "b": 408.84329, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*JOBCTL", "bbox": {"l": 424.93805, "t": 447.52255, "r": 433.26297000000005, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "QIBM_DB_SECADM", "bbox": {"l": 450.13806, "t": 401.6000700000001, "r": 458.46298, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "QIBM_DB_SQLADM", "bbox": {"l": 475.93835000000007, "t": 401.53442, "r": 484.26327999999995, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "QIBM_DB_SYSMON", "bbox": {"l": 501.13837, "t": 401.6145, "r": 509.46329, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "No Authority", "bbox": {"l": 526.39862, "t": 432.79944, "r": 534.72357, "b": 487.02005, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "SET CURRENT DEGREE", "bbox": {"l": 70.800003, "t": 498.69299, "r": 151.6794, "b": 506.66699, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " (SQL statement)", "bbox": {"l": 151.6803, "t": 498.55798, "r": 220.15681000000004, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "X", "bbox": {"l": 429.0, "t": 498.55798, "r": 435.00299000000007, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 480.00031, "t": 498.55798, "r": 486.0033, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "CHGQRYA", "bbox": {"l": 70.800018, "t": 517.65329, "r": 102.23972, "b": 525.62729, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": " command targeting a different user\u2019s job", "bbox": {"l": 102.23972, "t": 517.51828, "r": 264.5538, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "X", "bbox": {"l": 429.00003, "t": 517.51828, "r": 435.00302000000005, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "X", "bbox": {"l": 480.00034, "t": 517.51828, "r": 486.00333, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "STRDBMON", "bbox": {"l": 70.800049, "t": 536.67299, "r": 106.73975, "b": 544.64699, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": " or ", "bbox": {"l": 106.73975, "t": 536.5379800000001, "r": 119.77895, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "ENDDBMON", "bbox": {"l": 119.69975000000001, "t": 536.67299, "r": 155.69974, "b": 544.64699, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": " commands targeting a different user\u2019s job", "bbox": {"l": 155.69974, "t": 536.5379800000001, "r": 322.50574, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "X", "bbox": {"l": 429.00003, "t": 536.5379800000001, "r": 435.00302000000005, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "X", "bbox": {"l": 480.00034, "t": 536.5379800000001, "r": 486.00333, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "STRDBMON", "bbox": {"l": 70.800049, "t": 555.69269, "r": 106.73975, "b": 563.66669, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": " or ", "bbox": {"l": 106.73975, "t": 555.55768, "r": 119.77895, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ENDDBMON", "bbox": {"l": 119.69975000000001, "t": 555.69269, "r": 155.69974, "b": 563.66669, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": " commands targeting a job that matches the current user", "bbox": {"l": 155.69974, "t": 555.55768, "r": 381.02185, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "X", "bbox": {"l": 429.00003, "t": 555.55768, "r": 435.00302000000005, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "X", "bbox": {"l": 480.00034, "t": 555.55768, "r": 486.00333, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "X", "bbox": {"l": 505.26061999999996, "t": 555.55768, "r": 511.26361, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "X", "bbox": {"l": 530.76031, "t": 555.55768, "r": 536.76331, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "QUSRJOBI() API format 900 or System i Navigator\u2019s SQL Details for Job", "bbox": {"l": 70.800049, "t": 574.51797, "r": 359.51736, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "X", "bbox": {"l": 429.0000600000001, "t": 574.51797, "r": 435.00305000000003, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "X", "bbox": {"l": 480.00037, "t": 574.51797, "r": 486.00335999999993, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "X", "bbox": {"l": 505.2606799999999, "t": 574.51797, "r": 511.26367, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Visual Explain within Run SQL scripts", "bbox": {"l": 70.800079, "t": 593.5376699999999, "r": 220.75178999999997, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "X", "bbox": {"l": 429.0000600000001, "t": 593.5376699999999, "r": 435.00305000000003, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "X", "bbox": {"l": 480.00037, "t": 593.5376699999999, "r": 486.00335999999993, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "X", "bbox": {"l": 505.2606799999999, "t": 593.5376699999999, "r": 511.26367, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "X", "bbox": {"l": 530.76038, "t": 593.5376699999999, "r": 536.76337, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "Visual Explain outside of Run SQL scripts", "bbox": {"l": 70.800079, "t": 612.55737, "r": 236.6548, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "X", "bbox": {"l": 429.0000600000001, "t": 612.55737, "r": 435.00305000000003, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "X", "bbox": {"l": 480.00037, "t": 612.55737, "r": 486.00335999999993, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "ANALYZE PLAN CACHE procedure", "bbox": {"l": 70.800079, "t": 631.51767, "r": 213.12968, "b": 639.84268, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "X", "bbox": {"l": 429.0000600000001, "t": 631.51767, "r": 435.00305000000003, "b": 639.84268, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "X", "bbox": {"l": 480.00037, "t": 631.51767, "r": 486.00335999999993, "b": 639.84268, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "DUMP PLAN CACHE procedure", "bbox": {"l": 70.800079, "t": 650.53737, "r": 199.87808, "b": 658.86238, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "X", "bbox": {"l": 429.0000600000001, "t": 650.53737, "r": 435.00305000000003, "b": 658.86238, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "X", "bbox": {"l": 480.00037, "t": 650.53737, "r": 486.00335999999993, "b": 658.86238, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "MODIFY PLAN CACHE procedure", "bbox": {"l": 70.800079, "t": 669.55708, "r": 208.36777, "b": 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"coord_origin": "TOPLEFT"}}, {"id": 77, "text": "X", "bbox": {"l": 429.0000600000001, "t": 707.537071, "r": 435.00305000000003, "b": 715.862068, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "X", "bbox": {"l": 480.00037, "t": 707.537071, "r": 486.00335999999993, "b": 715.862068, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 2, "label": "text", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9789126515388489, "cells": [{"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665539860725403, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i 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", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 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Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 2. Roles and separation of duties"}], "body": [{"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9789126515388489, "cells": [{"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa\u2019s job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665539860725403, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 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Row and Column Access Control"}]}}, {"page_no": 10, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "P1", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Proc1:", "bbox": {"l": 148.4301, "t": 473.58524, "r": 174.05859, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Owner = JOE", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "SET OPTION USRPRF=*OWNER", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "USER = ALICE", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "CURRENT USER = JOE", "bbox": {"l": 148.4301, "t": 533.30984, "r": 234.57686999999999, "b": 541.82059, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 566.15842, "r": 191.70256, "b": 574.66917, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 578.10333, "r": 232.56117, "b": 586.61409, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "caption", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.7875164747238159, "cells": [{"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253707528114319, "cells": [{"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 134.5463104248047, "t": 103.41889190673828, "r": 542.0460205078125, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 20, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445975422859192, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. 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When no adopted  authority is present, this has the same value as USER.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "SYSTEM_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The authorization ID that initiated the connection.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445975422859192, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE\u2019s authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748277068138123, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "picture", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 135.64837646484375, "t": 384.1736755371094, "r": 301.2367248535156, "b": 594.7566528320312, "coord_origin": "TOPLEFT"}, "confidence": 0.7221462726593018, "cells": [], "children": [{"id": 15, "label": "text", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}, "confidence": 0.7616674900054932, "cells": [{"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 40, "text": 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global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9130509495735168, "cells": [{"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "19"}, {"label": "page_footer", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9557498693466187, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}], "body": [{"label": "caption", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.7875164747238159, "cells": [{"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-1 summarizes these special registers and their values."}, {"label": "caption", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253707528114319, "cells": [{"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-1 Special registers and their corresponding values"}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 134.5463104248047, "t": 103.41889190673828, "r": 542.0460205078125, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. 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When no adopted  authority is present, this has the same value as USER.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "SYSTEM_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The authorization ID that initiated the connection.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445975422859192, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE\u2019s authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748277068138123, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "picture", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 135.64837646484375, "t": 384.1736755371094, "r": 301.2367248535156, "b": 594.7566528320312, "coord_origin": "TOPLEFT"}, "confidence": 0.7221462726593018, "cells": [], "children": [{"id": 15, "label": "text", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}, "confidence": 0.7616674900054932, "cells": [{"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 40, "text": 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[{"id": 50, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 578.10333, "r": 232.56117, "b": 586.61409, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274529814720154, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"id": 21, 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global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}], "headers": [{"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9130509495735168, "cells": [{"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "19"}, {"label": "page_footer", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9557498693466187, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Description", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CLIENT_HOST", "bbox": {"l": 70.800003, "t": 129.49834999999996, "r": 132.7209, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "VARCHAR(255)", "bbox": {"l": 202.89029, "t": 129.49834999999996, "r": 267.07651, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Host name of the current client as returned by the system", "bbox": {"l": 281.84732, "t": 129.49834999999996, "r": 510.17548, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "CLIENT_IPADDR", "bbox": {"l": 70.800018, "t": 148.51806999999997, "r": 140.66522, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "VARCHAR(128)", "bbox": {"l": 202.87231, "t": 148.51806999999997, "r": 267.07739, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "IP address of the current client as returned by the system", "bbox": {"l": 281.84549, "t": 148.51806999999997, "r": 509.60583, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "CLIENT_PORT ", "bbox": {"l": 70.800018, "t": 167.53778, "r": 134.98264, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "INTEGER", "bbox": {"l": 202.90294, "t": 167.53778, "r": 242.80084, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Port used by the current client to communicate with the server", "bbox": {"l": 281.79785, "t": 167.53778, "r": 527.59222, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "PACKAGE_NAME", "bbox": {"l": 70.800018, "t": 186.5575, "r": 143.50925, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "VARCHAR(128)", "bbox": {"l": 202.80576, "t": 186.5575, "r": 267.06937, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Name of the currently running package", "bbox": {"l": 281.85187, "t": 186.5575, "r": 436.57259999999997, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "PACKAGE_SCHEMA", "bbox": {"l": 70.800018, "t": 205.51782000000003, "r": 156.01654, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "VARCHAR(128)", "bbox": {"l": 202.83545, "t": 205.51782000000003, "r": 267.08646, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Schema name of the currently running package", "bbox": {"l": 281.87076, "t": 205.51782000000003, "r": 470.44678, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "PACKAGE_VERSION", "bbox": {"l": 70.800018, "t": 224.53754000000004, "r": 157.89932, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "VARCHAR(64)", "bbox": {"l": 202.72472, "t": 224.53754000000004, "r": 261.98254, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Version identifier of the currently running package", "bbox": {"l": 281.74924, "t": 224.53754000000004, "r": 478.8438100000001, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ROUTINE_SCHEMA", "bbox": {"l": 70.800018, "t": 243.55724999999995, "r": 154.41992, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "VARCHAR(128)", "bbox": {"l": 202.79312, "t": 243.55724999999995, "r": 267.09274, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Schema name of the currently running routine", "bbox": {"l": 281.87164, "t": 243.55724999999995, "r": 464.26022, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ROUTINE_SPECIFIC_NAME", "bbox": {"l": 70.800018, "t": 262.51757999999995, "r": 188.43991, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "VARCHAR(128)", "bbox": {"l": 202.84441, "t": 262.51757999999995, "r": 267.03693, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Name of the currently running routine", "bbox": {"l": 281.80682, "t": 262.51757999999995, "r": 430.40045, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "ROUTINE_TYPE", "bbox": {"l": 70.800034, "t": 281.53726, "r": 139.43135, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "CHAR(1)", "bbox": {"l": 202.74635, "t": 281.53726, "r": 239.28996000000004, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Type of the currently running routine", "bbox": {"l": 281.79065, "t": 281.53726, "r": 425.09131, "b": 289.86227, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 11, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8370980620384216, "cells": [{"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9132355451583862, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 63.55636978149414, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Description", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CLIENT_HOST", "bbox": {"l": 70.800003, "t": 129.49834999999996, "r": 132.7209, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "VARCHAR(255)", "bbox": {"l": 202.89029, "t": 129.49834999999996, "r": 267.07651, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Host name of the current client as returned by the system", "bbox": {"l": 281.84732, "t": 129.49834999999996, "r": 510.17548, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "CLIENT_IPADDR", "bbox": {"l": 70.800018, "t": 148.51806999999997, "r": 140.66522, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "VARCHAR(128)", "bbox": {"l": 202.87231, "t": 148.51806999999997, "r": 267.07739, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "IP address of the current client as returned by the system", "bbox": {"l": 281.84549, "t": 148.51806999999997, "r": 509.60583, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "CLIENT_PORT ", "bbox": {"l": 70.800018, "t": 167.53778, "r": 134.98264, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "INTEGER", "bbox": {"l": 202.90294, "t": 167.53778, "r": 242.80084, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Port used by the current client to communicate with the server", "bbox": {"l": 281.79785, "t": 167.53778, "r": 527.59222, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "PACKAGE_NAME", "bbox": {"l": 70.800018, "t": 186.5575, "r": 143.50925, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "VARCHAR(128)", "bbox": {"l": 202.80576, "t": 186.5575, "r": 267.06937, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Name of the currently running package", "bbox": {"l": 281.85187, "t": 186.5575, "r": 436.57259999999997, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "PACKAGE_SCHEMA", "bbox": {"l": 70.800018, "t": 205.51782000000003, "r": 156.01654, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "VARCHAR(128)", "bbox": {"l": 202.83545, "t": 205.51782000000003, "r": 267.08646, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Schema name of the currently running package", "bbox": {"l": 281.87076, "t": 205.51782000000003, "r": 470.44678, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "PACKAGE_VERSION", "bbox": {"l": 70.800018, "t": 224.53754000000004, "r": 157.89932, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "VARCHAR(64)", "bbox": {"l": 202.72472, "t": 224.53754000000004, "r": 261.98254, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Version identifier of the currently running package", "bbox": {"l": 281.74924, "t": 224.53754000000004, "r": 478.8438100000001, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ROUTINE_SCHEMA", "bbox": {"l": 70.800018, "t": 243.55724999999995, "r": 154.41992, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "VARCHAR(128)", "bbox": {"l": 202.79312, "t": 243.55724999999995, "r": 267.09274, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Schema name of the currently running routine", "bbox": {"l": 281.87164, "t": 243.55724999999995, "r": 464.26022, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ROUTINE_SPECIFIC_NAME", "bbox": {"l": 70.800018, "t": 262.51757999999995, "r": 188.43991, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "VARCHAR(128)", "bbox": {"l": 202.84441, "t": 262.51757999999995, "r": 267.03693, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Name of the currently running routine", "bbox": {"l": 281.80682, "t": 262.51757999999995, "r": 430.40045, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "ROUTINE_TYPE", "bbox": {"l": 70.800034, "t": 281.53726, "r": 139.43135, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "CHAR(1)", "bbox": {"l": 202.74635, "t": 281.53726, "r": 239.28996000000004, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Type of the currently running routine", "bbox": {"l": 281.79065, "t": 281.53726, "r": 425.09131, "b": 289.86227, "coord_origin": "TOPLEFT"}}], "children": [{"id": 14, "label": "text", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 31, "text": "Description", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}}], 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129.49834999999996, "r": 510.17548, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 70.800018, "t": 148.51806999999997, "r": 140.66522, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 35, "text": "CLIENT_IPADDR", "bbox": {"l": 70.800018, "t": 148.51806999999997, "r": 140.66522, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 202.87231, "t": 148.51806999999997, "r": 267.07739, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 36, "text": "VARCHAR(128)", "bbox": {"l": 202.87231, "t": 148.51806999999997, "r": 267.07739, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 281.84549, "t": 148.51806999999997, "r": 509.60583, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 37, 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Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237534403800964, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338628649711609, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514462947845459, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512302875518799, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.706649661064148, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9187921285629272, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9505080580711365, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 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"coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237534403800964, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338628649711609, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514462947845459, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512302875518799, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.706649661064148, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"label": "page_footer", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9187921285629272, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "20"}, {"label": "page_footer", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9505080580711365, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8370980620384216, "cells": [{"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 lists the nine built-in global variables."}, {"label": "caption", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9132355451583862, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 63.55636978149414, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, 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Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237534403800964, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338628649711609, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514462947845459, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512302875518799, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.706649661064148, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}], "headers": [{"label": "page_footer", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9187921285629272, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "20"}, {"label": "page_footer", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9505080580711365, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606333613395691, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897271037101746, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785220146179199, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678944706916809, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467734694480896, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705167412757874, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469641447067261, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003496885299683, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.848820149898529, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411225080490112, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.910578727722168, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606333613395691, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897271037101746, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785220146179199, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678944706916809, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467734694480896, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705167412757874, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469641447067261, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003496885299683, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.848820149898529, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411225080490112, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.910578727722168, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "27"}, {"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}], "body": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606333613395691, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897271037101746, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785220146179199, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678944706916809, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467734694480896, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705167412757874, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469641447067261, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003496885299683, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.848820149898529, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411225080490112, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}], "headers": [{"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.910578727722168, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "27"}, {"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}]}}, {"page_no": 13, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 63.80192184448242, "t": 95.38238525390625, "r": 547.11474609375, "b": 170.0321044921875, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570101976394653, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.600240170955658, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985856056213379, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449256062507629, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.9049987792969, "r": 530.0478515625, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801458716392517, "cells": [], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.80192184448242, "t": 95.38238525390625, "r": 547.11474609375, "b": 170.0321044921875, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570101976394653, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.600240170955658, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985856056213379, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Column Access Control (masks)"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "*/"}, {"label": "text", "id": 16, "page_no": 13, "cluster": {"id": 16, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 17, "page_no": 13, "cluster": {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 18, "page_no": 13, "cluster": {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449256062507629, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.9049987792969, "r": 530.0478515625, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801458716392517, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.80192184448242, "t": 95.38238525390625, "r": 547.11474609375, "b": 170.0321044921875, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570101976394653, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.600240170955658, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985856056213379, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Column Access Control (masks)"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "*/"}, {"label": "text", "id": 16, "page_no": 13, "cluster": {"id": 16, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 17, "page_no": 13, "cluster": {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 18, "page_no": 13, "cluster": {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449256062507629, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.9049987792969, "r": 530.0478515625, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801458716392517, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}], "headers": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 14, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "77", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "2.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 145.19554, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "bbox": {"l": 147.9942, "t": 71.50903000000005, "r": 513.35919, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "enabled. It is clear that the implementation of the SQL statement is more complex ", "bbox": {"l": 151.19975, "t": 83.50885000000017, "r": 514.04858, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "because the row permission rule becomes part of the ", "bbox": {"l": 151.19975, "t": 95.50867000000005, "r": 389.64822, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHERE", "bbox": {"l": 389.57941, "t": 95.65808000000015, "r": 414.53918, "b": 104.48266999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": " clause.", "bbox": {"l": 414.59991, "t": 95.50867000000005, "r": 448.8892200000001, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Figure 4-68 Visual Explain with RCAC enabled", "bbox": {"l": 136.8, "t": 480.55798, "r": 327.09329, "b": 488.883, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "3.", "bbox": {"l": 136.8, "t": 506.56863, "r": 145.17432, "b": 515.78162, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "bbox": {"l": 147.96574, "t": 506.56863, "r": 543.63715, "b": 515.78162, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "bbox": {"l": 151.20016, "t": 518.56845, "r": 547.23944, "b": 527.78143, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "enabled. The index being advised is for the ORDER BY clause.", "bbox": {"l": 151.20016, "t": 530.5682400000001, "r": 430.28333, "b": 539.78125, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Figure 4-69 Index advice with no RCAC", "bbox": {"l": 64.800003, "t": 667.5179, "r": 227.10149, "b": 675.8429, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 3, "label": "list_item", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 514.04858, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9761855006217957, "cells": [{"id": 2, "text": "2.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 145.19554, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "bbox": {"l": 147.9942, "t": 71.50903000000005, "r": 513.35919, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "enabled. 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Figure 4-69 shows the index advice for the SQL statement without RCAC ", "bbox": {"l": 151.20016, "t": 518.56845, "r": 547.23944, "b": 527.78143, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "enabled. 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Implementing Row and Column Access Control: Banking example ", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "list_item", "id": 3, "page_no": 14, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 514.04858, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9761855006217957, "cells": [{"id": 2, "text": "2.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 145.19554, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "bbox": {"l": 147.9942, "t": 71.50903000000005, "r": 513.35919, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "enabled. 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It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause."}, {"label": "picture", "id": 0, "page_no": 14, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 136.5016632080078, "t": 119.24909210205078, "r": 545.4508666992188, "b": 477.54119873046875, "coord_origin": "TOPLEFT"}, "confidence": 0.9864527583122253, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 5, "page_no": 14, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 136.8, "t": 480.55798, "r": 327.09329, "b": 488.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9529654383659363, "cells": [{"id": 8, "text": "Figure 4-68 Visual Explain with RCAC enabled", "bbox": {"l": 136.8, "t": 480.55798, "r": 327.09329, "b": 488.883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4-68 Visual Explain with RCAC enabled"}, {"label": "list_item", "id": 2, "page_no": 14, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.8, "t": 506.56863, "r": 547.23944, "b": 539.78125, "coord_origin": "TOPLEFT"}, "confidence": 0.9766737818717957, "cells": [{"id": 9, "text": "3.", "bbox": {"l": 136.8, "t": 506.56863, "r": 145.17432, "b": 515.78162, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "bbox": {"l": 147.96574, "t": 506.56863, "r": 543.63715, "b": 515.78162, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "bbox": {"l": 151.20016, "t": 518.56845, "r": 547.23944, "b": 527.78143, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "enabled. The index being advised is for the ORDER BY clause.", "bbox": {"l": 151.20016, "t": 530.5682400000001, "r": 430.28333, "b": 539.78125, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause."}, {"label": "picture", "id": 1, "page_no": 14, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 64.27847290039062, "t": 553.5814819335938, "r": 506.39263916015625, "b": 664.0870971679688, "coord_origin": "TOPLEFT"}, "confidence": 0.9797717928886414, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 6, "page_no": 14, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 64.800003, "t": 667.5179, "r": 227.10149, "b": 675.8429, "coord_origin": "TOPLEFT"}, "confidence": 0.9514288306236267, "cells": [{"id": 13, "text": "Figure 4-69 Index advice with no RCAC", "bbox": {"l": 64.800003, "t": 667.5179, "r": 227.10149, "b": 675.8429, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4-69 Index advice with no RCAC"}, {"label": "page_footer", "id": 7, "page_no": 14, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9056528806686401, "cells": [{"id": 1, "text": "77", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "77"}, {"label": "page_footer", "id": 4, "page_no": 14, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9557602405548096, "cells": [{"id": 0, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}], "body": [{"label": "list_item", "id": 3, "page_no": 14, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 514.04858, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9761855006217957, "cells": [{"id": 2, "text": "2.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 145.19554, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "bbox": {"l": 147.9942, "t": 71.50903000000005, "r": 513.35919, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "enabled. It is clear that the implementation of the SQL statement is more complex ", "bbox": {"l": 151.19975, "t": 83.50885000000017, "r": 514.04858, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "because the row permission rule becomes part of the ", "bbox": {"l": 151.19975, "t": 95.50867000000005, "r": 389.64822, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHERE", "bbox": {"l": 389.57941, "t": 95.65808000000015, "r": 414.53918, "b": 104.48266999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": " clause.", "bbox": {"l": 414.59991, "t": 95.50867000000005, "r": 448.8892200000001, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause."}, {"label": "picture", "id": 0, "page_no": 14, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 136.5016632080078, "t": 119.24909210205078, "r": 545.4508666992188, "b": 477.54119873046875, "coord_origin": "TOPLEFT"}, "confidence": 0.9864527583122253, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 5, "page_no": 14, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 136.8, "t": 480.55798, "r": 327.09329, "b": 488.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9529654383659363, "cells": [{"id": 8, "text": "Figure 4-68 Visual Explain with RCAC enabled", "bbox": {"l": 136.8, "t": 480.55798, "r": 327.09329, "b": 488.883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4-68 Visual Explain with RCAC enabled"}, {"label": "list_item", "id": 2, "page_no": 14, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.8, "t": 506.56863, "r": 547.23944, "b": 539.78125, "coord_origin": "TOPLEFT"}, "confidence": 0.9766737818717957, "cells": [{"id": 9, "text": "3.", "bbox": {"l": 136.8, "t": 506.56863, "r": 145.17432, "b": 515.78162, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "bbox": {"l": 147.96574, "t": 506.56863, "r": 543.63715, "b": 515.78162, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "bbox": {"l": 151.20016, "t": 518.56845, "r": 547.23944, "b": 527.78143, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "enabled. The index being advised is for the ORDER BY clause.", "bbox": {"l": 151.20016, "t": 530.5682400000001, "r": 430.28333, "b": 539.78125, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause."}, {"label": "picture", "id": 1, "page_no": 14, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 64.27847290039062, "t": 553.5814819335938, "r": 506.39263916015625, "b": 664.0870971679688, "coord_origin": "TOPLEFT"}, "confidence": 0.9797717928886414, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 6, "page_no": 14, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 64.800003, "t": 667.5179, "r": 227.10149, "b": 675.8429, "coord_origin": "TOPLEFT"}, "confidence": 0.9514288306236267, "cells": [{"id": 13, "text": "Figure 4-69 Index advice with no RCAC", "bbox": {"l": 64.800003, "t": 667.5179, "r": 227.10149, "b": 675.8429, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4-69 Index advice with no RCAC"}], "headers": [{"label": "page_footer", "id": 7, "page_no": 14, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9056528806686401, "cells": [{"id": 1, "text": "77", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "77"}, {"label": "page_footer", "id": 4, "page_no": 14, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9557602405548096, "cells": [{"id": 0, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}]}}, {"page_no": 15, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632163763046265, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8994000554084778, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632163763046265, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}, {"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8994000554084778, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "124"}, {"label": "page_footer", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632163763046265, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . 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diff --git a/tests/data/groundtruth/docling_v2/2203.01017v2.doctags.txt b/tests/data/groundtruth/docling_v2/2203.01017v2.doctags.txt
index 605f6c2f..1f840bdd 100644
--- a/tests/data/groundtruth/docling_v2/2203.01017v2.doctags.txt
+++ b/tests/data/groundtruth/docling_v2/2203.01017v2.doctags.txt
@@ -6,15 +6,14 @@
 <section_header_level_1><location><page_1><loc_52><loc_71><loc_67><loc_72></location>a. Picture of a table:</section_header_level_1>
 <section_header_level_1><location><page_1><loc_8><loc_30><loc_21><loc_32></location>1. Introduction</section_header_level_1>
 <text><location><page_1><loc_8><loc_10><loc_47><loc_29></location>The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.</text>
-<table>
-<location><page_1><loc_54><loc_65><loc_75><loc_70></location>
-<caption>Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.</caption>
-<row_0><col_0><body></col_0><col_1><col_header>3</col_1></row_0>
-<row_1><col_0><body>2</col_0><col_1><body></col_1></row_1>
-</table>
 <figure>
 <location><page_1><loc_52><loc_62><loc_88><loc_71></location>
 </figure>
+<table>
+<location><page_1><loc_52><loc_62><loc_88><loc_71></location>
+<caption>Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.</caption>
+<row_0><col_0><col_header>3</col_0><col_1><col_header>1</col_1></row_0>
+</table>
 <unordered_list>
 <list_item><location><page_1><loc_52><loc_58><loc_79><loc_60></location>b. Red-annotation of bounding boxes, Blue-predictions by TableFormer</list_item>
 </unordered_list>
@@ -24,18 +23,18 @@
 <unordered_list>
 <list_item><location><page_1><loc_52><loc_46><loc_80><loc_47></location>c. Structure predicted by TableFormer:</list_item>
 </unordered_list>
-<table>
-<location><page_1><loc_52><loc_38><loc_81><loc_45></location>
-<row_0><col_0><body>0</col_0><col_1><body>1 2</col_1><col_2><body>1</col_2></row_0>
-<row_1><col_0><body>3 4</col_0><col_1><body>5 3</col_1><col_2><body>6</col_2></row_1>
-<row_2><col_0><body>9</col_0><col_1><body>10</col_1><col_2><body>11</col_2></row_2>
-<row_3><col_0><body>8 13 2</col_0><col_1><body>14</col_1><col_2><body>15</col_2></row_3>
-<row_4><col_0><body>17</col_0><col_1><body>18</col_1><col_2><body>19</col_2></row_4>
-</table>
 <figure>
 <location><page_1><loc_52><loc_37><loc_88><loc_45></location>
-<caption>Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.</caption>
 </figure>
+<table>
+<location><page_1><loc_52><loc_37><loc_88><loc_45></location>
+<caption>Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.</caption>
+<row_0><col_0><col_header>0</col_0><col_1><col_header>1</col_1><col_2><col_header>1</col_2><col_3><col_header>2 1</col_3><col_4><col_header>2 1</col_4><col_5><body></col_5></row_0>
+<row_1><col_0><body>3</col_0><col_1><body>4</col_1><col_2><body>5 3</col_2><col_3><body>6</col_3><col_4><body>7</col_4><col_5><body></col_5></row_1>
+<row_2><col_0><body>8</col_0><col_1><body>9</col_1><col_2><body>10</col_2><col_3><body>11</col_3><col_4><body>12</col_4><col_5><body>2</col_5></row_2>
+<row_3><col_0><body></col_0><col_1><body>13</col_1><col_2><body>14</col_2><col_3><body>15</col_3><col_4><body>16</col_4><col_5><body>2</col_5></row_3>
+<row_4><col_0><body></col_0><col_1><body>17</col_1><col_2><body>18</col_2><col_3><body>19</col_3><col_4><body>20</col_4><col_5><body>2</col_5></row_4>
+</table>
 <text><location><page_1><loc_50><loc_16><loc_89><loc_26></location>Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.</text>
 <text><location><page_1><loc_50><loc_10><loc_89><loc_16></location>The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be</text>
 <text><location><page_2><loc_8><loc_88><loc_47><loc_91></location>considered as a solved problem, given enough ground-truth data to train on.</text>
@@ -73,7 +72,7 @@
 <text><location><page_4><loc_8><loc_21><loc_47><loc_45></location>Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain.</text>
 <text><location><page_4><loc_8><loc_10><loc_47><loc_20></location>In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third</text>
 <table>
-<location><page_4><loc_52><loc_80><loc_88><loc_91></location>
+<location><page_4><loc_51><loc_80><loc_89><loc_91></location>
 <caption>Table 1: Both "Combined-Tabnet" and "CombinedTabnet" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.</caption>
 <row_0><col_0><body></col_0><col_1><col_header>Tags</col_1><col_2><col_header>Bbox</col_2><col_3><col_header>Size</col_3><col_4><col_header>Format</col_4></row_0>
 <row_1><col_0><row_header>PubTabNet</col_0><col_1><body>3</col_1><col_2><body>3</col_2><col_3><body>509k</col_3><col_4><body>PNG</col_4></row_1>
@@ -128,7 +127,7 @@
 <section_header_level_1><location><page_7><loc_8><loc_70><loc_28><loc_72></location>5.4. Quantitative Analysis</section_header_level_1>
 <text><location><page_7><loc_8><loc_50><loc_47><loc_69></location>Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.</text>
 <table>
-<location><page_7><loc_11><loc_27><loc_46><loc_48></location>
+<location><page_7><loc_9><loc_26><loc_46><loc_48></location>
 <caption>Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).</caption>
 <row_0><col_0><col_header>Model</col_0><col_1><col_header>Dataset</col_1><col_2><col_header>Simple</col_2><col_3><col_header>TEDS Complex</col_3><col_4><col_header>All</col_4></row_0>
 <row_1><col_0><row_header>EDD</col_0><col_1><body>PTN</col_1><col_2><body>91.1</col_2><col_3><body>88.7</col_3><col_4><body>89.9</col_4></row_1>
@@ -146,7 +145,7 @@
 <text><location><page_7><loc_8><loc_10><loc_47><loc_19></location>Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate</text>
 <text><location><page_7><loc_50><loc_71><loc_89><loc_91></location>our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.</text>
 <table>
-<location><page_7><loc_53><loc_62><loc_86><loc_68></location>
+<location><page_7><loc_50><loc_62><loc_87><loc_69></location>
 <caption>Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.</caption>
 <row_0><col_0><col_header>Model</col_0><col_1><col_header>Dataset</col_1><col_2><col_header>mAP</col_2><col_3><col_header>mAP (PP)</col_3></row_0>
 <row_1><col_0><body>EDD+BBox</col_0><col_1><body>PubTabNet</col_1><col_2><body>79.2</col_2><col_3><body>82.7</col_3></row_1>
@@ -155,9 +154,9 @@
 </table>
 <text><location><page_7><loc_50><loc_34><loc_89><loc_54></location>Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations.</text>
 <table>
-<location><page_7><loc_56><loc_19><loc_84><loc_31></location>
+<location><page_7><loc_54><loc_19><loc_85><loc_32></location>
 <caption>Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.</caption>
-<row_0><col_0><col_header>Model</col_0><col_1><col_header>Simple</col_1><col_2><col_header>TEDS Complex</col_2><col_3><col_header>All</col_3></row_0>
+<row_0><col_0><body>Model</col_0><col_1><col_header>Simple</col_1><col_2><col_header>TEDS Complex</col_2><col_3><col_header>All</col_3></row_0>
 <row_1><col_0><row_header>Tabula</col_0><col_1><body>78.0</col_1><col_2><body>57.8</col_2><col_3><body>67.9</col_3></row_1>
 <row_2><col_0><row_header>Traprange</col_0><col_1><body>60.8</col_1><col_2><body>49.9</col_2><col_3><body>55.4</col_3></row_2>
 <row_3><col_0><row_header>Camelot</col_0><col_1><body>80.0</col_1><col_2><body>66.0</col_2><col_3><body>73.0</col_3></row_3>
@@ -179,9 +178,9 @@
 <caption>b. Structure predicted by TableFormer, with superimposed matched PDF cell text:</caption>
 </figure>
 <table>
-<location><page_8><loc_9><loc_63><loc_48><loc_72></location>
+<location><page_8><loc_9><loc_63><loc_49><loc_72></location>
 <row_0><col_0><body></col_0><col_1><body></col_1><col_2><col_header>論文ファイル</col_2><col_3><col_header>論文ファイル</col_3><col_4><col_header>参考文献</col_4><col_5><col_header>参考文献</col_5></row_0>
-<row_1><col_0><body>出典</col_0><col_1><col_header>ファイル 数</col_1><col_2><col_header>英語</col_2><col_3><col_header>日本語</col_3><col_4><col_header>英語</col_4><col_5><col_header>日本語</col_5></row_1>
+<row_1><col_0><col_header>出典</col_0><col_1><col_header>ファイル 数</col_1><col_2><col_header>英語</col_2><col_3><col_header>日本語</col_3><col_4><col_header>英語</col_4><col_5><col_header>日本語</col_5></row_1>
 <row_2><col_0><row_header>Association for Computational Linguistics(ACL2003)</col_0><col_1><body>65</col_1><col_2><body>65</col_2><col_3><body>0</col_3><col_4><body>150</col_4><col_5><body>0</col_5></row_2>
 <row_3><col_0><row_header>Computational Linguistics(COLING2002)</col_0><col_1><body>140</col_1><col_2><body>140</col_2><col_3><body>0</col_3><col_4><body>150</col_4><col_5><body>0</col_5></row_3>
 <row_4><col_0><row_header>電気情報通信学会 2003 年総合大会</col_0><col_1><body>150</col_1><col_2><body>8</col_2><col_3><body>142</col_3><col_4><body>223</col_4><col_5><body>147</col_5></row_4>
@@ -192,7 +191,7 @@
 <row_9><col_0><body></col_0><col_1><body>945</col_1><col_2><body>294</col_2><col_3><body>651</col_3><col_4><body>1122</col_4><col_5><body>955</col_5></row_9>
 </table>
 <table>
-<location><page_8><loc_50><loc_64><loc_89><loc_72></location>
+<location><page_8><loc_50><loc_64><loc_90><loc_72></location>
 <caption>Text is aligned to match original for ease of viewing</caption>
 <row_0><col_0><body></col_0><col_1><col_header>Shares (in millions)</col_1><col_2><col_header>Shares (in millions)</col_2><col_3><col_header>Weighted Average Grant Date Fair Value</col_3><col_4><col_header>Weighted Average Grant Date Fair Value</col_4></row_0>
 <row_1><col_0><body></col_0><col_1><col_header>RS U s</col_1><col_2><col_header>PSUs</col_2><col_3><col_header>RSUs</col_3><col_4><col_header>PSUs</col_4></row_1>
diff --git a/tests/data/groundtruth/docling_v2/2203.01017v2.json b/tests/data/groundtruth/docling_v2/2203.01017v2.json
index 63c34e8e..0d0d328b 100644
--- a/tests/data/groundtruth/docling_v2/2203.01017v2.json
+++ b/tests/data/groundtruth/docling_v2/2203.01017v2.json
@@ -1 +1 @@
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"name": "list", "label": "list"}, {"self_ref": "#/groups/14", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/490"}, {"cref": "#/texts/491"}, {"cref": "#/texts/492"}, {"cref": "#/texts/493"}, {"cref": "#/texts/494"}, {"cref": "#/texts/495"}, {"cref": "#/texts/496"}], "name": "list", "label": "list"}, {"self_ref": "#/groups/15", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/498"}], "name": "list", "label": "list"}], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 18.340221405029297, "t": 584.1799926757812, "r": 36.339778900146484, "b": 231.99996948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022"}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 96.3010025024414, "t": 684.9658813476562, "r": 498.9270935058594, "b": 672.0686645507812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "TableFormer: Table Structure Understanding with Transformers.", "text": "TableFormer: Table Structure Understanding with Transformers.", "level": 1}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 142.4770050048828, "t": 645.3146362304688, "r": 452.7502746582031, "b": 620.6796264648438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research", "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research", "level": 1}, {"self_ref": "#/texts/3", "parent": {"cref": "#/groups/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 208.123, "t": 616.03876, "r": 378.73257, "b": 607.57446, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "{ ahn,nli,mly,taa } @zurich.ibm.com", "text": "{ ahn,nli,mly,taa } @zurich.ibm.com"}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 145.99497985839844, "t": 576.5170288085938, "r": 190.48028564453125, "b": 565.769287109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Abstract", "text": "Abstract", "level": 1}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 315.5670166015625, "t": 573.9931640625, "r": 408.4407043457031, "b": 565.2451782226562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "a. Picture of a table:", "text": "a. Picture of a table:", "level": 1}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 252.05723571777344, "r": 126.94803619384766, "b": 241.30950927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "1. Introduction", "text": "1. Introduction", "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 231.216796875, "r": 286.3650817871094, "b": 78.84822082519531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 712]}], "orig": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.", "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 550.6049194335938, "r": 286.3651123046875, "b": 279.00335693359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1320]}], "orig": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"self_ref": "#/texts/9", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 451.9457100000001, "t": 556.65295, "r": 457.95050000000003, "b": 546.52252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/10", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 384.0329, "t": 539.32104, "r": 390.03769, "b": 529.19061, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.19681, "t": 522.64734, "r": 337.2016, "b": 512.51691, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/12", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 1, "bbox": {"l": 315.5670166015625, "t": 478.3052062988281, "r": 486.4019470214844, "b": 458.7572021484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer", "text": "b. 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Structure predicted by TableFormer:", "text": "c. Structure predicted by TableFormer:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/39", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 308.86199951171875, "t": 277.4996337890625, "r": 545.1151733398438, "b": 232.7270965576172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 220]}], "orig": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.", "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'."}, {"self_ref": "#/texts/40", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 459.87621999999993, "t": 354.4064, "r": 465.88101, "b": 344.276, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/41", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 318.88071, "t": 354.31412, "r": 323.27319, "b": 345.52917, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "0", "text": "0"}, {"self_ref": "#/texts/42", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 347.24872, "t": 354.31412, "r": 351.6412, "b": 345.52917, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/43", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": 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"14"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 440.95941000000005, "t": 318.29575, "r": 449.7435, "b": 309.5108, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15"}, {"self_ref": "#/texts/59", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 487.81491, "t": 318.29575, "r": 496.599, "b": 309.5108, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "16", "text": "16"}, {"self_ref": "#/texts/60", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 347.24872, "t": 306.87531, "r": 356.03281, "b": 298.09036, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "17", "text": "17"}, {"self_ref": "#/texts/61", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 394.10422, "t": 306.87531, "r": 402.88831, "b": 298.09036, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "18", "text": "18"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 440.95941000000005, "t": 306.87531, "r": 449.7435, "b": 298.09036, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "19", "text": "19"}, {"self_ref": "#/texts/63", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 487.81491, "t": 306.87531, "r": 496.599, "b": 298.09036, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "20", "text": "20"}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 308.86199951171875, "t": 207.59063720703125, "r": 545.1151733398438, "b": 126.95307159423828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 363]}], "orig": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.", "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 308.86199951171875, "t": 123.61963653564453, "r": 545.1151123046875, "b": 78.84806823730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 229]}], "orig": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be", "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 1, "bbox": {"l": 295.1210021972656, "t": 57.866634368896484, "r": 300.102294921875, "b": 48.9600715637207, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.36505126953125, "b": 695.9300537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "considered as a solved problem, given enough ground-truth data to train on.", "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 692.4285888671875, "r": 286.3651428222656, "b": 563.9699096679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 626]}], "orig": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image.", "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 560.4684448242188, "r": 286.3651123046875, "b": 420.054931640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 643]}], "orig": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image.", "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 416.5534973144531, "r": 286.3665771484375, "b": 359.8269958496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 242]}], "orig": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:", "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.56901550292969, "t": 347.568115234375, "r": 286.3648986816406, "b": 302.6770324707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 166]}], "orig": "\u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "text": "\u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.56901550292969, "t": 289.9661560058594, "r": 286.3648986816406, "b": 245.0740509033203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 181]}], "orig": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "text": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569000244140625, "t": 232.3631591796875, "r": 286.36492919921875, "b": 199.4270477294922, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "\u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "text": "\u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/74", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569007873535156, "t": 186.5966033935547, "r": 286.3650817871094, "b": 153.779052734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 131]}], "orig": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "text": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11200714111328, "t": 141.401611328125, "r": 286.3651123046875, "b": 96.63004302978516, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe", "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 60.97100067138672, "t": 86.40372467041016, "r": 183.7305450439453, "b": 79.27845764160156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "$^{1}$https://github.com/IBM/SynthTabNet", "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 2, "bbox": {"l": 295.1210021972656, "t": 57.86671829223633, "r": 300.102294921875, "b": 48.96015548706055, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 716.7916259765625, "r": 545.1151123046875, "b": 683.9750366210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 166]}], "orig": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community.", "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"self_ref": "#/texts/79", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 670.26806640625, "r": 498.28021240234375, "b": 659.5203247070312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "2. Previous work and State of the Art", "text": "2. Previous work and State of the Art", "level": 1}, {"self_ref": "#/texts/80", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 649.7786254882812, "r": 545.1151733398438, "b": 461.54498291015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 901]}], "orig": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc.", "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 458.4305419921875, "r": 545.115234375, "b": 341.9270935058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 552]}], "orig": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification.", "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.8619689941406, "t": 338.9322204589844, "r": 545.1168823242188, "b": 78.84815216064453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1262]}], "orig": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \"image-encoder \u2192 text-decoder\" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \"image-encoder \u2192 dual decoder\" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the", "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \"image-encoder \u2192 text-decoder\" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \"image-encoder \u2192 dual decoder\" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 250.15101623535156, "b": 707.8850708007812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "tag-decoder which is constrained to the table-tags.", "text": "tag-decoder which is constrained to the table-tags."}, {"self_ref": "#/texts/84", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199951171875, "t": 704.7806396484375, "r": 286.3651428222656, "b": 516.5458984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 864]}], "orig": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper.", "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"self_ref": "#/texts/85", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199188232422, "t": 513.56103515625, "r": 286.3651123046875, "b": 301.297119140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1007]}], "orig": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18].", "text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 298.3112487792969, "r": 286.36627197265625, "b": 169.733154296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 619]}], "orig": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered.", "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"self_ref": "#/texts/87", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 156.05516052246094, "r": 105.22545623779297, "b": 145.30743408203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "3. Datasets", "text": "3. Datasets", "level": 1}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 135.57470703125, "r": 286.3650817871094, "b": 78.84813690185547, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 281]}], "orig": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-", "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 3, "bbox": {"l": 295.1210021972656, "t": 57.86680221557617, "r": 300.102294921875, "b": 48.96023941040039, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/90", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 524.1636352539062, "r": 545.1151123046875, "b": 503.3020935058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/pictures/3"}, "children": [], "label": 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"coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Rows / Columns", "text": "Rows / Columns"}, {"self_ref": "#/texts/124", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 474.5266418457031, "r": 437.27001953125, "b": 465.6200866699219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "balance in the previous datasets.", "text": "balance in the previous datasets."}, {"self_ref": "#/texts/125", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 460.4686279296875, "r": 545.1151733398438, "b": 164.6382598876953, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1400]}], "orig": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \"simple\" when it does not contain row spans or column spans, otherwise it is \"complex\". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits.", "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \"simple\" when it does not contain row spans or column spans, otherwise it is \"complex\". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"self_ref": "#/texts/126", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 159.48580932617188, "r": 545.1151123046875, "b": 78.84823608398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 406]}], "orig": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small", "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"self_ref": "#/texts/127", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.3651123046875, "b": 695.9300537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 93]}], "orig": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns).", "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"self_ref": "#/texts/128", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 691.0396118164062, "r": 286.3651428222656, "b": 478.8949279785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 983]}], "orig": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"self_ref": "#/texts/129", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 474.0044860839844, "r": 286.3651123046875, "b": 357.50103759765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 571]}], "orig": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data.", "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"self_ref": "#/texts/130", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 352.610595703125, "r": 286.3665466308594, "b": 164.37611389160156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 941]}], "orig": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain.", "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"self_ref": "#/texts/131", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11201477050781, "t": 159.4856719970703, "r": 286.3651123046875, "b": 78.84810638427734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 405]}], "orig": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third", "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"self_ref": "#/texts/132", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 295.1209716796875, "t": 57.86674880981445, "r": 300.1022644042969, "b": 48.96018600463867, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/133", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 624.338623046875, "r": 545.1150512695312, "b": 567.6110229492188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 267]}], "orig": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.", "text": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"self_ref": "#/texts/134", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 542.3795776367188, "r": 545.1151733398438, "b": 497.6080322265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 210]}], "orig": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples.", "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"self_ref": "#/texts/135", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 320.8169860839844, "t": 494.22760009765625, "r": 542.7439575195312, "b": 485.321044921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"self_ref": "#/texts/136", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 470.8160400390625, "r": 444.9360656738281, "b": 460.0683288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "4. The TableFormer model", "text": "4. The TableFormer model", "level": 1}, {"self_ref": "#/texts/137", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 450.06060791015625, "r": 545.115234375, "b": 345.5131530761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 504]}], "orig": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required.", "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"self_ref": "#/texts/138", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 334.30572509765625, "r": 420.16058349609375, "b": 324.45367431640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "4.1. Model architecture.", "text": "4.1. Model architecture.", "level": 1}, {"self_ref": "#/texts/139", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.8619689941406, "t": 315.2347106933594, "r": 545.11572265625, "b": 127.00019073486328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 907]}], "orig": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification.", "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"self_ref": "#/texts/140", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.8619689941406, "t": 123.73930358886719, "r": 545.1151123046875, "b": 78.84818267822266, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 223]}], "orig": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-", "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}, {"self_ref": "#/texts/141", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 50.11199188232422, "t": 588.0142211914062, "r": 545.1084594726562, "b": 567.0330810546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 212]}], "orig": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.", "text": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure."}, {"self_ref": "#/texts/142", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 331.03699, "t": 713.44019, "r": 352.12589, "b": 707.69958, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "BBoxes", "text": "BBoxes"}, {"self_ref": "#/texts/143", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 251.76939000000002, "t": 711.0690300000001, "r": 266.39557, "b": 705.32843, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "BBox", "text": "BBox"}, {"self_ref": "#/texts/144", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 247.51601, "t": 705.96899, "r": 270.65021, "b": 700.22839, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Decoder", "text": "Decoder"}, {"self_ref": "#/texts/145", "parent": {"cref": 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[{"page_no": 5, "bbox": {"l": 54.235424, "t": 418.18768, "r": 59.30449699999999, "b": 413.54578000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "x2", "text": "x2"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 150.18539, "t": 416.33157, "r": 197.31964, "b": 410.66956, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "Multi-Head Attention", "text": "Multi-Head Attention"}, {"self_ref": "#/texts/226", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 239.54543, "t": 409.78656, "r": 258.08942, "b": 404.12454, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Sigmoid", "text": "Sigmoid"}, {"self_ref": "#/texts/227", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 54.14704100000001, "t": 407.12817, "r": 59.51152, "b": 342.21674, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "Transformer Encoder Network", "text": "Transformer Encoder Network"}, {"self_ref": "#/texts/228", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 78.513298, "t": 400.68143, "r": 84.644547, "b": 395.01941, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "Add", "text": "Add"}, {"self_ref": "#/texts/229", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 116.52705, "t": 400.68143, "r": 125.11079999999998, "b": 395.01941, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "& Normalisation", "text": "& Normalisation"}, {"self_ref": "#/texts/230", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 150.55193, "t": 397.5766, "r": 156.68318, "b": 391.91458, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "Add", "text": "Add"}, {"self_ref": "#/texts/231", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 188.56567, "t": 397.5766, "r": 197.14943, "b": 391.91458, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "& Normalisation", "text": "& Normalisation"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 228.44568000000004, "t": 386.85318, "r": 238.73892, "b": 381.19116, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "MLP", "text": "MLP"}, {"self_ref": "#/texts/233", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 256.29767, "t": 386.7967499999999, "r": 271.77792, "b": 381.13474, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Linear", "text": "Linear"}, {"self_ref": "#/texts/234", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 147.86377, "t": 369.90665, "r": 199.76227, "b": 364.24463, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "Feed Forward Network", "text": "Feed Forward Network"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 76.024773, "t": 367.54691, "r": 127.92327000000002, "b": 361.88489, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "Feed Forward Network", "text": "Feed Forward Network"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 227.81598999999997, "t": 353.94458, "r": 270.78442, "b": 348.10794, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Bounding Boxes &", "text": "Bounding Boxes &"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 150.55193, "t": 351.75152999999995, "r": 156.68318, "b": 346.08951, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "Add", "text": "Add"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 188.56567, "t": 351.75152999999995, "r": 197.14943, "b": 346.08951, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "& Normalisation", "text": "& Normalisation"}, {"self_ref": "#/texts/239", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 233.70262, "t": 347.93817, "r": 263.51105, "b": 342.1095000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Classification", "text": "Classification"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 78.382828, "t": 347.11044, "r": 84.514076, "b": 341.44843, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "Add", "text": "Add"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 116.39658, "t": 347.11044, "r": 124.98033, "b": 341.44843, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "& Normalisation", "text": "& Normalisation"}, {"self_ref": "#/texts/242", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 167.46945, "t": 329.55676, "r": 181.6292, "b": 323.89474, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Linear", "text": "Linear"}, {"self_ref": "#/texts/243", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 165.61292, "t": 313.52893, "r": 184.43242, "b": 307.86691, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Softmax", "text": "Softmax"}, {"self_ref": "#/texts/244", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 85.295891, "t": 307.46811, "r": 122.16431, "b": 301.63312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Encoded Output", "text": "Encoded Output"}, {"self_ref": "#/texts/245", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 157.17369, "t": 291.6969, "r": 190.41711, "b": 285.87057, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Predicted Tags", "text": "Predicted Tags"}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.86199951171875, "t": 542.465576171875, "r": 545.1150512695312, "b": 497.69305419921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 227]}], "orig": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder .", "text": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder ."}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 494.6601867675781, "r": 545.1151123046875, "b": 378.0381774902344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 563]}], "orig": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \"Scene Understanding\", \"Image Captioning\"), something which we relate to the simplicity of table images.", "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \"Scene Understanding\", \"Image Captioning\"), something which we relate to the simplicity of table images."}, {"self_ref": "#/texts/248", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619689941406, "t": 374.8857421875, "r": 545.1151123046875, "b": 246.4272918701172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 592]}], "orig": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score.", "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"self_ref": "#/texts/249", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 243.39540100097656, "r": 545.1151123046875, "b": 138.727294921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 483]}], "orig": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query.", "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query."}, {"self_ref": "#/texts/250", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 135.57484436035156, "r": 545.1150512695312, "b": 78.84827423095703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 286]}], "orig": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-", "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}, {"self_ref": "#/texts/251", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 295.1209411621094, "t": 57.86684036254883, "r": 300.10223388671875, "b": 48.96027755737305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/252", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.3651428222656, "b": 636.1539916992188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 380]}], "orig": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence.", "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"self_ref": "#/texts/253", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 632.3755493164062, "r": 286.3651123046875, "b": 551.7369384765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 371]}], "orig": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.", "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"self_ref": "#/texts/254", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 548.0780639648438, "r": 286.36572265625, "b": 347.76910400390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 985]}], "orig": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.", "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"self_ref": "#/texts/255", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112022399902344, "t": 343.9896545410156, "r": 286.364990234375, "b": 323.12811279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "The loss used to train the TableFormer can be defined as following:", "text": "The loss used to train the TableFormer can be defined as following:"}, {"self_ref": "#/texts/256", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 6, "bbox": {"l": 124.33001708984375, "t": 298.71905517578125, "r": 286.3624267578125, "b": 274.92828369140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 - \u03bb ) l$_{box}$ (1)", "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 - \u03bb ) l$_{box}$ (1)"}, {"self_ref": "#/texts/257", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112030029296875, "t": 261.4079895019531, "r": 281.596923828125, "b": 251.78411865234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters.", "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"self_ref": "#/texts/258", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 236.08311462402344, "r": 171.9833526611328, "b": 225.33538818359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "5. Experimental Results", "text": "5. Experimental Results", "level": 1}, {"self_ref": "#/texts/259", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 215.7356719970703, "r": 179.17501831054688, "b": 205.8836212158203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "5.1. Implementation Details", "text": "5.1. Implementation Details", "level": 1}, {"self_ref": "#/texts/260", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 196.2656707763672, "r": 286.36517333984375, "b": 151.4931182861328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 207]}], "orig": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:", "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"self_ref": "#/texts/261", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 6, "bbox": {"l": 91.66104888916016, "t": 138.1719970703125, "r": 286.3624572753906, "b": 113.60411834716797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)", "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"self_ref": "#/texts/262", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112060546875, "t": 99.70968627929688, "r": 286.3651428222656, "b": 78.8481216430664, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 117]}], "orig": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved", "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"self_ref": "#/texts/263", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 295.12103271484375, "t": 57.86667251586914, "r": 300.1023254394531, "b": 48.96010971069336, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6"}, {"self_ref": "#/texts/264", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862060546875, "t": 716.7916870117188, "r": 545.115234375, "b": 683.97509765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 156]}], "orig": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.", "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"self_ref": "#/texts/265", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862060546875, "t": 675.7706298828125, "r": 545.1152954101562, "b": 463.6259460449219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1024]}], "orig": "The Transformer Encoder consists of two \"Transformer Encoder Layers\", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \"Transformer Decoder Layers\" with similar input and output dimensions as the \"Transformer Encoder Layers\". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5.", "text": "The Transformer Encoder consists of two \"Transformer Encoder Layers\", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \"Transformer Decoder Layers\" with similar input and output dimensions as the \"Transformer Encoder Layers\". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"self_ref": "#/texts/266", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 455.4224853515625, "r": 545.1151733398438, "b": 362.83001708984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 419]}], "orig": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence.", "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"self_ref": "#/texts/267", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 354.6255798339844, "r": 545.115234375, "b": 238.12310791015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 528]}], "orig": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag.", "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"self_ref": "#/texts/268", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 212.4456787109375, "r": 397.44281005859375, "b": 202.5936279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "5.2. Generalization", "text": "5.2. Generalization", "level": 1}, {"self_ref": "#/texts/269", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 188.55067443847656, "r": 545.1151733398438, "b": 119.86811065673828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 299]}], "orig": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively.", "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"self_ref": "#/texts/270", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 111.6646728515625, "r": 545.115234375, "b": 78.84710693359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 155]}], "orig": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized.", "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"self_ref": "#/texts/271", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 717.5986328125, "r": 167.89825439453125, "b": 707.74658203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "5.3. Datasets and Metrics", "text": "5.3. Datasets and Metrics", "level": 1}, {"self_ref": "#/texts/272", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 698.6495971679688, "r": 286.3651123046875, "b": 653.8770141601562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 192]}], "orig": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:", "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"self_ref": "#/texts/273", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 7, "bbox": {"l": 86.218994140625, "t": 641.6820068359375, "r": 286.3623962402344, "b": 619.26123046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 99]}], "orig": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 - EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)", "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 - EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"self_ref": "#/texts/274", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11198425292969, "t": 610.9970092773438, "r": 286.36285400390625, "b": 578.02099609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 162]}], "orig": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .", "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"self_ref": "#/texts/275", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 567.1805419921875, "r": 170.45169067382812, "b": 557.3284912109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "5.4. Quantitative Analysis", "text": "5.4. Quantitative Analysis", "level": 1}, {"self_ref": "#/texts/276", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 548.35009765625, "r": 286.3651428222656, "b": 395.862060546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 723]}], "orig": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.", "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"self_ref": "#/texts/277", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 199.56663513183594, "r": 286.3651123046875, "b": 178.705078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).", "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"self_ref": "#/texts/278", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 175.65663146972656, "r": 261.7873229980469, "b": 166.7500762939453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "FT: Model was trained on PubTabNet then finetuned.", "text": "FT: Model was trained on PubTabNet then finetuned."}, {"self_ref": "#/texts/279", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11201477050781, "t": 147.6501922607422, "r": 286.3659973144531, "b": 78.84806823730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 346]}], "orig": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate", "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"self_ref": "#/texts/280", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 295.1210021972656, "t": 57.866641998291016, "r": 300.102294921875, "b": 48.960079193115234, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/281", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 716.7916259765625, "r": 545.1151733398438, "b": 564.4229125976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 737]}], "orig": "our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.", "text": "our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"self_ref": "#/texts/282", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 475.5506896972656, "r": 545.1151733398438, "b": 454.68914794921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.", "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"self_ref": "#/texts/283", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 308.8619689941406, "t": 424.3202819824219, "r": 545.1156616210938, "b": 271.8323059082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 715]}], "orig": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations.", "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"self_ref": "#/texts/284", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 135.13864135742188, "r": 545.1151733398438, "b": 102.32206726074219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 148]}], "orig": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.", "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}, {"self_ref": "#/texts/285", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 53.28603744506836, "t": 713.3124389648438, "r": 61.550289154052734, "b": 705.4392700195312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "a.", "text": "a.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/286", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 65.68241882324219, "t": 713.3124389648438, "r": 499.5556335449219, "b": 705.4392700195312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 105]}], "orig": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "text": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/287", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 53.81178283691406, "t": 697.7188720703125, "r": 284.3459167480469, "b": 689.845703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Japanese language (previously unseen by TableFormer):", "text": "Japanese language (previously unseen by TableFormer):", "level": 1}, {"self_ref": "#/texts/288", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 304.830810546875, "t": 697.7188720703125, "r": 431.0911865234375, "b": 689.845703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "Example table from FinTabNet:", "text": "Example table from FinTabNet:", "level": 1}, {"self_ref": "#/texts/289", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 53.81178283691406, "t": 583.7667236328125, "r": 385.93450927734375, "b": 575.8935546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 79]}], "orig": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:"}, {"self_ref": "#/texts/290", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 380.42730712890625, "t": 499.69573974609375, "r": 549.4217529296875, "b": 493.39715576171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Text is aligned to match original for ease of viewing", "text": "Text is aligned to match original for ease of viewing"}, {"self_ref": "#/texts/291", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 471.1226501464844, "r": 545.11376953125, "b": 426.3501281738281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset."}, {"self_ref": "#/texts/292", "parent": {"cref": "#/pictures/8"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.715248, "t": 410.22278, "r": 85.657333, "b": 405.55719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Ground Truth", "text": "Ground Truth"}, {"self_ref": "#/texts/293", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 220.26282, "t": 410.22278, "r": 342.07819, "b": 405.55719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "Red - PDF cells, Green - predicted bounding boxes", "text": "Red - PDF cells, Green - predicted bounding boxes"}, {"self_ref": "#/texts/294", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 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"children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 300.6046447753906, "r": 163.75579833984375, "b": 290.7525939941406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "5.5. Qualitative Analysis", "text": "5.5. Qualitative Analysis", "level": 1}, {"self_ref": "#/texts/351", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 255.1266326904297, "r": 286.3651123046875, "b": 78.84805297851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 866]}], "orig": "We showcase several visualizations for the different components of our network on various \"complex\" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.", "text": "We showcase several visualizations for the different components of our network on various \"complex\" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"self_ref": "#/texts/352", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 308.86199951171875, "t": 301.29107666015625, "r": 460.8484802246094, "b": 290.5433654785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "6. Future Work & Conclusion", "text": "6. Future Work & Conclusion", "level": 1}, {"self_ref": "#/texts/353", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 308.86199951171875, "t": 279.10662841796875, "r": 545.1151733398438, "b": 138.69407653808594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 640]}], "orig": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \"SynthTabNet\" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets.", "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \"SynthTabNet\" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"self_ref": "#/texts/354", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 308.86199951171875, "t": 119.90107727050781, "r": 364.4058532714844, "b": 109.15335845947266, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "References", "text": "References", "level": 1}, {"self_ref": "#/texts/355", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 313.3450012207031, "t": 98.0382080078125, "r": 545.1134033203125, "b": 79.06324768066406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 121]}], "orig": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/399", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 132.8419952392578, "t": 681.4251098632812, "r": 465.37591552734375, "b": 656.4699096679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 83]}], "orig": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "level": 1}, {"self_ref": "#/texts/400", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 630.839111328125, "r": 175.96437072753906, "b": 620.0913696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "1. Details on the datasets", "text": "1. Details on the datasets", "level": 1}, {"self_ref": "#/texts/401", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 611.0206909179688, "r": 150.364013671875, "b": 601.1686401367188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "1.1. Data preparation", "text": "1.1. Data preparation", "level": 1}, {"self_ref": "#/texts/402", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 592.0797119140625, "r": 286.3651428222656, "b": 403.8451843261719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 931]}], "orig": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length.", "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length."}, {"self_ref": "#/texts/403", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 400.5947265625, "r": 286.3651123046875, "b": 164.54029846191406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1149]}], "orig": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"self_ref": "#/texts/404", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 161.28985595703125, "r": 286.3649597167969, "b": 140.42730712890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset.", "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"self_ref": "#/texts/405", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 129.60986328125, "r": 153.60784912109375, "b": 119.7578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "1.2. Synthetic datasets", "text": "1.2. Synthetic datasets", "level": 1}, {"self_ref": "#/texts/406", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 110.66886901855469, "r": 286.36505126953125, "b": 77.852294921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 167]}], "orig": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-", "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"self_ref": "#/texts/407", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 629.3448486328125, "r": 545.1151123046875, "b": 584.572265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 221]}], "orig": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).", "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"self_ref": "#/texts/408", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 580.7648315429688, "r": 545.1150512695312, "b": 559.9032592773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 89]}], "orig": "The process of generating a synthetic dataset can be decomposed into the following steps:", "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"self_ref": "#/texts/409", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 556.0947875976562, "r": 545.1151123046875, "b": 475.45721435546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 373]}], "orig": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/410", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 471.6497802734375, "r": 545.1151733398438, "b": 343.19134521484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/411", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 339.3839111328125, "r": 545.1151733398438, "b": 294.61138916015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 195]}], "orig": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/412", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 290.803955078125, "r": 545.1152954101562, "b": 246.0314178466797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 218]}], "orig": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/413", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 242.22396850585938, "r": 545.1151733398438, "b": 185.4964141845703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 238]}], "orig": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/414", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 169.70941162109375, "r": 545.1087646484375, "b": 145.01368713378906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "2. Prediction post-processing for PDF documents", "text": "2. Prediction post-processing for PDF documents", "level": 1}, {"self_ref": "#/texts/415", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.8620300292969, "t": 134.57896423339844, "r": 545.1151733398438, "b": 77.85139465332031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 247]}], "orig": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:", "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"self_ref": "#/texts/416", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 292.63104248046875, "t": 57.86696243286133, "r": 302.5936279296875, "b": 48.96039962768555, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/417", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 626.4976196289062, "r": 545.1137084960938, "b": 605.6360473632812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 245]}], "orig": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "text": "Figure 7: Distribution of the tables across different dimensions per dataset. 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650.28937, "r": 328.80933, "b": 645.33215, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Simple", "text": "Simple"}, {"self_ref": "#/texts/476", "parent": {"cref": "#/pictures/11"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 185.37759, "t": 650.28882, "r": 202.84102, "b": 645.3316, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Simple", "text": "Simple"}, {"self_ref": "#/texts/477", "parent": {"cref": "#/groups/11"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 61.569000244140625, "t": 581.068603515625, "r": 286.3651123046875, "b": 560.20703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "\u00b7 TableFormer output does not include the table cell content.", "text": "\u00b7 TableFormer output does not include the table cell content.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/478", "parent": {"cref": "#/groups/11"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 61.569000244140625, "t": 547.9285888671875, "r": 286.3651428222656, "b": 527.0670166015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "text": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/479", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 512.7965698242188, "r": 286.3651123046875, "b": 396.2931213378906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 545]}], "orig": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes.", "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"self_ref": "#/texts/480", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 508.6367492675781, "r": 545.1151123046875, "b": 404.08929443359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 471]}], "orig": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/481", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 392.9306640625, "r": 286.3649597167969, "b": 372.068115234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "Here is a step-by-step description of the prediction postprocessing:", "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"self_ref": "#/texts/482", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 368.7046813964844, "r": 286.3650817871094, "b": 335.8881530761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 173]}], "orig": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/483", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 332.52471923828125, "r": 286.36505126953125, "b": 287.7532043457031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 187]}], "orig": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/484", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 284.3897705078125, "r": 286.36492919921875, "b": 263.5272216796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones.", "text": "3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/485", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 260.164794921875, "r": 286.3651123046875, "b": 227.34722900390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 131]}], "orig": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/486", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 223.98377990722656, "r": 286.3650817871094, "b": 191.16722106933594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 169]}], "orig": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/487", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 220.66197204589844, "r": 545.1168823242188, "b": 187.8454132080078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 113]}], "orig": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row).", "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"self_ref": "#/texts/488", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 12, "bbox": {"l": 110.70498657226562, "t": 168.5640869140625, "r": 286.3623962402344, "b": 137.89439392089844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } - min { x$_{c}$ } (4)", "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } - min { x$_{c}$ } (4)"}, {"self_ref": "#/texts/489", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 124.6520767211914, "r": 286.36199951171875, "b": 103.07321166992188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 103]}], "orig": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point.", "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"self_ref": "#/texts/490", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 123.969970703125, "r": 545.114990234375, "b": 103.10841369628906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 107]}], "orig": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "text": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/491", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 99.70977783203125, "r": 286.3649597167969, "b": 78.84821319580078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 110]}], "orig": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/492", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 99.70997619628906, "r": 545.1151733398438, "b": 78.84840393066406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 118]}], "orig": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/493", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 184.44696044921875, "r": 545.1150512695312, "b": 163.58441162109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "text": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/494", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 160.18597412109375, "r": 545.1150512695312, "b": 127.3694076538086, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 117]}], "orig": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/495", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 400.6898498535156, "r": 545.1151733398438, "b": 332.00836181640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 311]}], "orig": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/496", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 328.6089172363281, "r": 545.1151733398438, "b": 224.06141662597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 503]}], "orig": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/497", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 308.86199951171875, "t": 581.0687866210938, "r": 545.1151733398438, "b": 536.2962036132812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 183]}], "orig": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.", "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"self_ref": "#/texts/498", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.86199951171875, "t": 532.8977661132812, "r": 545.114990234375, "b": 512.0361938476562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 91]}], "orig": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/499", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 292.6310729980469, "t": 57.86697006225586, "r": 302.5936584472656, "b": 48.96040725708008, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "12", "text": "12"}, {"self_ref": "#/texts/500", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 88.84658813476562, "b": 707.8850708007812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "phan cell.", "text": "phan cell."}, {"self_ref": "#/texts/501", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 704.8366088867188, "r": 286.3649597167969, "b": 683.9750366210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "9f. Otherwise create a new structural cell and match it wit the orphan cell.", "text": "9f. Otherwise create a new structural cell and match it wit the orphan cell."}, {"self_ref": "#/texts/502", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 680.8369140625, "r": 286.364990234375, "b": 660.2941284179688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "Aditional images with examples of TableFormer predictions and post-processing can be found below.", "text": "Aditional images with examples of TableFormer predictions and post-processing can be found below."}, {"self_ref": "#/texts/503", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 63.340999603271484, "t": 289.9436340332031, "r": 273.1334228515625, "b": 281.0370788574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Figure 8: Example of a table with multi-line header.", "text": "Figure 8: Example of a table with multi-line header."}, {"self_ref": "#/texts/504", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 292.6309814453125, "t": 57.866641998291016, "r": 302.59356689453125, "b": 48.960079193115234, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "13", "text": "13"}, {"self_ref": "#/texts/505", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 308.86199951171875, "t": 485.4016418457031, "r": 545.1151123046875, "b": 464.54010009765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "Figure 9: Example of a table with big empty distance between cells.", "text": "Figure 9: Example of a table with big empty distance between cells."}, {"self_ref": "#/texts/506", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 312.3429870605469, "t": 111.50663757324219, "r": 541.63232421875, "b": 102.60006713867188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "Figure 10: Example of a complex table with empty cells.", "text": "Figure 10: Example of a complex table with empty cells."}, {"self_ref": "#/texts/507", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 50.11199951171875, "t": 435.2296447753906, "r": 286.3650817871094, "b": 414.36810302734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "Figure 11: Simple table with different style and empty cells.", "text": "Figure 11: Simple table with different style and empty cells."}, {"self_ref": "#/texts/508", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 54.61899948120117, "t": 120.181640625, "r": 281.85589599609375, "b": 111.27507781982422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "Figure 12: Simple table predictions and post processing.", "text": "Figure 12: Simple table predictions and post processing."}, {"self_ref": "#/texts/509", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 292.6309814453125, "t": 57.86663818359375, "r": 302.59356689453125, "b": 48.96007537841797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "14", "text": "14"}, {"self_ref": "#/texts/510", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 315.7900085449219, "t": 420.3156433105469, "r": 538.1852416992188, "b": 411.4090881347656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "Figure 13: Table predictions example on colorful table.", "text": "Figure 13: Table predictions example on colorful table."}, {"self_ref": "#/texts/511", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 344.9849853515625, "t": 108.45364379882812, "r": 508.9893493652344, "b": 99.54707336425781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Figure 14: Example with multi-line text.", "text": "Figure 14: Example with multi-line text."}, {"self_ref": "#/texts/512", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 84.23300170898438, "t": 147.64862060546875, "r": 252.24224853515625, "b": 138.7420654296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Figure 15: Example with triangular table.", "text": "Figure 15: Example with triangular table."}, {"self_ref": "#/texts/513", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 292.6309814453125, "t": 57.86665725708008, "r": 302.59356689453125, "b": 48.9600944519043, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15"}, {"self_ref": "#/texts/514", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 308.8619689941406, "t": 139.0646514892578, "r": 545.1151123046875, "b": 118.20308685302734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.", "text": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact."}, {"self_ref": "#/texts/515", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 16, "bbox": {"l": 50.11199951171875, "t": 283.6626281738281, "r": 545.1138305664062, "b": 262.80108642578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.", "text": "Figure 17: Example of long table. 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ahn,nli,mly,taa } @zurich.ibm.com", "text": "{ ahn,nli,mly,taa } @zurich.ibm.com"}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 145.99497985839844, "t": 576.5170288085938, "r": 190.48028564453125, "b": 565.769287109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Abstract", "text": "Abstract", "level": 1}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 315.5670166015625, "t": 573.9931640625, "r": 408.4407043457031, "b": 565.2451782226562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "a. Picture of a table:", "text": "a. Picture of a table:", "level": 1}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 252.05723571777344, "r": 126.94803619384766, "b": 241.30950927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "1. Introduction", "text": "1. Introduction", "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 231.216796875, "r": 286.3650817871094, "b": 78.84822082519531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 712]}], "orig": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.", "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 451.9457100000001, "t": 556.65295, "r": 457.95050000000003, "b": 546.52252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/9", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 384.0329, "t": 539.32104, "r": 390.03769, "b": 529.19061, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/10", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.19681, "t": 522.64734, "r": 337.2016, "b": 512.51691, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 550.6049194335938, "r": 286.3651123046875, "b": 279.00335693359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1320]}], "orig": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"self_ref": "#/texts/12", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 1, "bbox": {"l": 315.5670166015625, "t": 478.3052062988281, "r": 486.4019470214844, "b": 458.7572021484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer", "text": "b. 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with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.", "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'."}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 308.86199951171875, "t": 207.59063720703125, "r": 545.1151733398438, "b": 126.95307159423828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 363]}], "orig": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.", "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 308.86199951171875, "t": 123.61963653564453, "r": 545.1151123046875, "b": 78.84806823730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 229]}], "orig": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be", "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 1, "bbox": {"l": 295.1210021972656, "t": 57.866634368896484, "r": 300.102294921875, "b": 48.9600715637207, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.36505126953125, "b": 695.9300537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "considered as a solved problem, given enough ground-truth data to train on.", "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 692.4285888671875, "r": 286.3651428222656, "b": 563.9699096679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 626]}], "orig": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image.", "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 560.4684448242188, "r": 286.3651123046875, "b": 420.054931640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 643]}], "orig": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image.", "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 416.5534973144531, "r": 286.3665771484375, "b": 359.8269958496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 242]}], "orig": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:", "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.56901550292969, "t": 347.568115234375, "r": 286.3648986816406, "b": 302.6770324707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 166]}], "orig": "\u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "text": "\u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.56901550292969, "t": 289.9661560058594, "r": 286.3648986816406, "b": 245.0740509033203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 181]}], "orig": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "text": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569000244140625, "t": 232.3631591796875, "r": 286.36492919921875, "b": 199.4270477294922, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "\u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "text": "\u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/74", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569007873535156, "t": 186.5966033935547, "r": 286.3650817871094, "b": 153.779052734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 131]}], "orig": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "text": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11200714111328, "t": 141.401611328125, "r": 286.3651123046875, "b": 96.63004302978516, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe", "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 60.97100067138672, "t": 86.40372467041016, "r": 183.7305450439453, "b": 79.27845764160156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "$^{1}$https://github.com/IBM/SynthTabNet", "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 2, "bbox": {"l": 295.1210021972656, "t": 57.86671829223633, "r": 300.102294921875, "b": 48.96015548706055, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 716.7916259765625, "r": 545.1151123046875, "b": 683.9750366210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 166]}], "orig": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community.", "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"self_ref": "#/texts/79", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 670.26806640625, "r": 498.28021240234375, "b": 659.5203247070312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "2. Previous work and State of the Art", "text": "2. Previous work and State of the Art", "level": 1}, {"self_ref": "#/texts/80", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 649.7786254882812, "r": 545.1151733398438, "b": 461.54498291015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 901]}], "orig": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc.", "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 458.4305419921875, "r": 545.115234375, "b": 341.9270935058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 552]}], "orig": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification.", "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.8619689941406, "t": 338.9322204589844, "r": 545.1168823242188, "b": 78.84815216064453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1262]}], "orig": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \"image-encoder \u2192 text-decoder\" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \"image-encoder \u2192 dual decoder\" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the", "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \"image-encoder \u2192 text-decoder\" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \"image-encoder \u2192 dual decoder\" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 250.15101623535156, "b": 707.8850708007812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "tag-decoder which is constrained to the table-tags.", "text": "tag-decoder which is constrained to the table-tags."}, {"self_ref": "#/texts/84", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199951171875, "t": 704.7806396484375, "r": 286.3651428222656, "b": 516.5458984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 864]}], "orig": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper.", "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"self_ref": "#/texts/85", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199188232422, "t": 513.56103515625, "r": 286.3651123046875, "b": 301.297119140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1007]}], "orig": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18].", "text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 298.3112487792969, "r": 286.36627197265625, "b": 169.733154296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 619]}], "orig": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered.", "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"self_ref": "#/texts/87", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 156.05516052246094, "r": 105.22545623779297, "b": 145.30743408203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "3. Datasets", "text": "3. Datasets", "level": 1}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 135.57470703125, "r": 286.3650817871094, "b": 78.84813690185547, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 281]}], "orig": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-", "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 3, "bbox": {"l": 295.1210021972656, "t": 57.86680221557617, "r": 300.102294921875, "b": 48.96023941040039, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/90", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 524.1636352539062, "r": 545.1151123046875, "b": 503.3020935058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/pictures/3"}, "children": [], "label": 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"coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Rows / Columns", "text": "Rows / Columns"}, {"self_ref": "#/texts/124", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 474.5266418457031, "r": 437.27001953125, "b": 465.6200866699219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "balance in the previous datasets.", "text": "balance in the previous datasets."}, {"self_ref": "#/texts/125", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 460.4686279296875, "r": 545.1151733398438, "b": 164.6382598876953, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1400]}], "orig": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \"simple\" when it does not contain row spans or column spans, otherwise it is \"complex\". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits.", "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \"simple\" when it does not contain row spans or column spans, otherwise it is \"complex\". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"self_ref": "#/texts/126", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 159.48580932617188, "r": 545.1151123046875, "b": 78.84823608398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 406]}], "orig": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small", "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"self_ref": "#/texts/127", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.3651123046875, "b": 695.9300537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 93]}], "orig": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns).", "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"self_ref": "#/texts/128", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 691.0396118164062, "r": 286.3651428222656, "b": 478.8949279785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 983]}], "orig": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"self_ref": "#/texts/129", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 474.0044860839844, "r": 286.3651123046875, "b": 357.50103759765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 571]}], "orig": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data.", "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"self_ref": "#/texts/130", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 352.610595703125, "r": 286.3665466308594, "b": 164.37611389160156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 941]}], "orig": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain.", "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"self_ref": "#/texts/131", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11201477050781, "t": 159.4856719970703, "r": 286.3651123046875, "b": 78.84810638427734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 405]}], "orig": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third", "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"self_ref": "#/texts/132", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 295.1209716796875, "t": 57.86674880981445, "r": 300.1022644042969, "b": 48.96018600463867, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/133", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 624.338623046875, "r": 545.1150512695312, "b": 567.6110229492188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 267]}], "orig": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.", "text": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"self_ref": "#/texts/134", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 542.3795776367188, "r": 545.1151733398438, "b": 497.6080322265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 210]}], "orig": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples.", "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"self_ref": "#/texts/135", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 320.8169860839844, "t": 494.22760009765625, "r": 542.7439575195312, "b": 485.321044921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"self_ref": "#/texts/136", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 470.8160400390625, "r": 444.9360656738281, "b": 460.0683288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "4. The TableFormer model", "text": "4. The TableFormer model", "level": 1}, {"self_ref": "#/texts/137", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 450.06060791015625, "r": 545.115234375, "b": 345.5131530761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 504]}], "orig": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required.", "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"self_ref": "#/texts/138", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 334.30572509765625, "r": 420.16058349609375, "b": 324.45367431640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "4.1. Model architecture.", "text": "4.1. Model architecture.", "level": 1}, {"self_ref": "#/texts/139", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.8619689941406, "t": 315.2347106933594, "r": 545.11572265625, "b": 127.00019073486328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 907]}], "orig": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification.", "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"self_ref": "#/texts/140", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.8619689941406, "t": 123.73930358886719, "r": 545.1151123046875, "b": 78.84818267822266, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 223]}], "orig": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-", "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}, {"self_ref": "#/texts/141", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 50.11199188232422, "t": 588.0142211914062, "r": 545.1084594726562, "b": 567.0330810546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 212]}], "orig": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.", "text": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. 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"orig": "...", "text": "..."}, {"self_ref": "#/texts/200", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 328.37479, "t": 613.74615, "r": 367.72333, "b": 608.00555, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Structure Tags", "text": "Structure Tags"}, {"self_ref": "#/texts/201", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 50.11199951171875, "t": 264.2171936035156, "r": 286.365966796875, "b": 111.72905731201172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 745]}], "orig": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.", "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. 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"text", "prov": [{"page_no": 5, "bbox": {"l": 85.295891, "t": 307.46811, "r": 122.16431, "b": 301.63312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Encoded Output", "text": "Encoded Output"}, {"self_ref": "#/texts/245", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 157.17369, "t": 291.6969, "r": 190.41711, "b": 285.87057, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Predicted Tags", "text": "Predicted Tags"}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.86199951171875, "t": 542.465576171875, "r": 545.1150512695312, "b": 497.69305419921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 227]}], "orig": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder .", "text": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder ."}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 494.6601867675781, "r": 545.1151123046875, "b": 378.0381774902344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 563]}], "orig": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \"Scene Understanding\", \"Image Captioning\"), something which we relate to the simplicity of table images.", "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \"Scene Understanding\", \"Image Captioning\"), something which we relate to the simplicity of table images."}, {"self_ref": "#/texts/248", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619689941406, "t": 374.8857421875, "r": 545.1151123046875, "b": 246.4272918701172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 592]}], "orig": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score.", "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"self_ref": "#/texts/249", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 243.39540100097656, "r": 545.1151123046875, "b": 138.727294921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 483]}], "orig": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query.", "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query."}, {"self_ref": "#/texts/250", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 135.57484436035156, "r": 545.1150512695312, "b": 78.84827423095703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 286]}], "orig": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-", "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}, {"self_ref": "#/texts/251", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 295.1209411621094, "t": 57.86684036254883, "r": 300.10223388671875, "b": 48.96027755737305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/252", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.3651428222656, "b": 636.1539916992188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 380]}], "orig": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence.", "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"self_ref": "#/texts/253", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 632.3755493164062, "r": 286.3651123046875, "b": 551.7369384765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 371]}], "orig": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.", "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"self_ref": "#/texts/254", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 548.0780639648438, "r": 286.36572265625, "b": 347.76910400390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 985]}], "orig": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.", "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"self_ref": "#/texts/255", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112022399902344, "t": 343.9896545410156, "r": 286.364990234375, "b": 323.12811279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "The loss used to train the TableFormer can be defined as following:", "text": "The loss used to train the TableFormer can be defined as following:"}, {"self_ref": "#/texts/256", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 6, "bbox": {"l": 124.33001708984375, "t": 298.71905517578125, "r": 286.3624267578125, "b": 274.92828369140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 - \u03bb ) l$_{box}$ (1)", "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 - \u03bb ) l$_{box}$ (1)"}, {"self_ref": "#/texts/257", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112030029296875, "t": 261.4079895019531, "r": 281.596923828125, "b": 251.78411865234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters.", "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"self_ref": "#/texts/258", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 236.08311462402344, "r": 171.9833526611328, "b": 225.33538818359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "5. Experimental Results", "text": "5. Experimental Results", "level": 1}, {"self_ref": "#/texts/259", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 215.7356719970703, "r": 179.17501831054688, "b": 205.8836212158203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "5.1. Implementation Details", "text": "5.1. Implementation Details", "level": 1}, {"self_ref": "#/texts/260", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 196.2656707763672, "r": 286.36517333984375, "b": 151.4931182861328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 207]}], "orig": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:", "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"self_ref": "#/texts/261", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 6, "bbox": {"l": 91.66104888916016, "t": 138.1719970703125, "r": 286.3624572753906, "b": 113.60411834716797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)", "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"self_ref": "#/texts/262", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112060546875, "t": 99.70968627929688, "r": 286.3651428222656, "b": 78.8481216430664, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 117]}], "orig": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved", "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"self_ref": "#/texts/263", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 295.12103271484375, "t": 57.86667251586914, "r": 300.1023254394531, "b": 48.96010971069336, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6"}, {"self_ref": "#/texts/264", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862060546875, "t": 716.7916870117188, "r": 545.115234375, "b": 683.97509765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 156]}], "orig": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.", "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"self_ref": "#/texts/265", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862060546875, "t": 675.7706298828125, "r": 545.1152954101562, "b": 463.6259460449219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1024]}], "orig": "The Transformer Encoder consists of two \"Transformer Encoder Layers\", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \"Transformer Decoder Layers\" with similar input and output dimensions as the \"Transformer Encoder Layers\". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5.", "text": "The Transformer Encoder consists of two \"Transformer Encoder Layers\", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \"Transformer Decoder Layers\" with similar input and output dimensions as the \"Transformer Encoder Layers\". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"self_ref": "#/texts/266", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 455.4224853515625, "r": 545.1151733398438, "b": 362.83001708984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 419]}], "orig": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence.", "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"self_ref": "#/texts/267", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 354.6255798339844, "r": 545.115234375, "b": 238.12310791015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 528]}], "orig": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag.", "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"self_ref": "#/texts/268", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 212.4456787109375, "r": 397.44281005859375, "b": 202.5936279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "5.2. Generalization", "text": "5.2. Generalization", "level": 1}, {"self_ref": "#/texts/269", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 188.55067443847656, "r": 545.1151733398438, "b": 119.86811065673828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 299]}], "orig": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively.", "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"self_ref": "#/texts/270", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 111.6646728515625, "r": 545.115234375, "b": 78.84710693359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 155]}], "orig": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized.", "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"self_ref": "#/texts/271", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 717.5986328125, "r": 167.89825439453125, "b": 707.74658203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "5.3. Datasets and Metrics", "text": "5.3. Datasets and Metrics", "level": 1}, {"self_ref": "#/texts/272", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 698.6495971679688, "r": 286.3651123046875, "b": 653.8770141601562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 192]}], "orig": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:", "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"self_ref": "#/texts/273", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 7, "bbox": {"l": 86.218994140625, "t": 641.6820068359375, "r": 286.3623962402344, "b": 619.26123046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 99]}], "orig": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 - EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)", "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 - EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"self_ref": "#/texts/274", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11198425292969, "t": 610.9970092773438, "r": 286.36285400390625, "b": 578.02099609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 162]}], "orig": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .", "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"self_ref": "#/texts/275", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 567.1805419921875, "r": 170.45169067382812, "b": 557.3284912109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "5.4. Quantitative Analysis", "text": "5.4. Quantitative Analysis", "level": 1}, {"self_ref": "#/texts/276", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 548.35009765625, "r": 286.3651428222656, "b": 395.862060546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 723]}], "orig": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.", "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"self_ref": "#/texts/277", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 199.56663513183594, "r": 286.3651123046875, "b": 178.705078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).", "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"self_ref": "#/texts/278", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 175.65663146972656, "r": 261.7873229980469, "b": 166.7500762939453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "FT: Model was trained on PubTabNet then finetuned.", "text": "FT: Model was trained on PubTabNet then finetuned."}, {"self_ref": "#/texts/279", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11201477050781, "t": 147.6501922607422, "r": 286.3659973144531, "b": 78.84806823730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 346]}], "orig": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate", "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"self_ref": "#/texts/280", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 295.1210021972656, "t": 57.866641998291016, "r": 300.102294921875, "b": 48.960079193115234, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/281", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 716.7916259765625, "r": 545.1151733398438, "b": 564.4229125976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 737]}], "orig": "our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.", "text": "our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"self_ref": "#/texts/282", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 475.5506896972656, "r": 545.1151733398438, "b": 454.68914794921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.", "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"self_ref": "#/texts/283", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 308.8619689941406, "t": 424.3202819824219, "r": 545.1156616210938, "b": 271.8323059082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 715]}], "orig": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations.", "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"self_ref": "#/texts/284", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 135.13864135742188, "r": 545.1151733398438, "b": 102.32206726074219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 148]}], "orig": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.", "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}, {"self_ref": "#/texts/285", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 53.28603744506836, "t": 713.3124389648438, "r": 61.550289154052734, "b": 705.4392700195312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "a.", "text": "a.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/286", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 65.68241882324219, "t": 713.3124389648438, "r": 499.5556335449219, "b": 705.4392700195312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 105]}], "orig": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "text": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/287", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 53.81178283691406, "t": 697.7188720703125, "r": 284.3459167480469, "b": 689.845703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Japanese language (previously unseen by TableFormer):", "text": "Japanese language (previously unseen by TableFormer):", "level": 1}, {"self_ref": "#/texts/288", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 304.830810546875, "t": 697.7188720703125, "r": 431.0911865234375, "b": 689.845703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "Example table from FinTabNet:", "text": "Example table from FinTabNet:", "level": 1}, {"self_ref": "#/texts/289", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 53.81178283691406, "t": 583.7667236328125, "r": 385.93450927734375, "b": 575.8935546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 79]}], "orig": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:"}, {"self_ref": "#/texts/290", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 380.42730712890625, "t": 499.69573974609375, "r": 549.4217529296875, "b": 493.39715576171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Text is aligned to match original for ease of viewing", "text": "Text is aligned to match original for ease of viewing"}, {"self_ref": "#/texts/291", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 471.1226501464844, "r": 545.11376953125, "b": 426.3501281738281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset."}, {"self_ref": "#/texts/292", "parent": {"cref": "#/pictures/8"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.715248, "t": 410.22278, "r": 85.657333, "b": 405.55719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Ground Truth", "text": "Ground Truth"}, {"self_ref": "#/texts/293", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 220.26282, "t": 410.22278, "r": 342.07819, "b": 405.55719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "Red - PDF cells, Green - predicted bounding boxes", "text": "Red - PDF cells, Green - predicted bounding boxes"}, {"self_ref": "#/texts/294", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 62.595001220703125, "t": 333.2716369628906, "r": 532.6304931640625, "b": 324.3650817871094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 112]}], "orig": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table."}, {"self_ref": "#/texts/295", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 384.35437, "t": 410.22278, "r": 430.99261, "b": 405.55719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "Predicted Structure", "text": "Predicted Structure"}, {"self_ref": "#/texts/296", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 385.2814, "t": 402.79996, "r": 388.44073, "b": 396.48132, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "0", "text": "0"}, {"self_ref": 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"children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 300.6046447753906, "r": 163.75579833984375, "b": 290.7525939941406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "5.5. Qualitative Analysis", "text": "5.5. Qualitative Analysis", "level": 1}, {"self_ref": "#/texts/351", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 255.1266326904297, "r": 286.3651123046875, "b": 78.84805297851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 866]}], "orig": "We showcase several visualizations for the different components of our network on various \"complex\" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.", "text": "We showcase several visualizations for the different components of our network on various \"complex\" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"self_ref": "#/texts/352", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 308.86199951171875, "t": 301.29107666015625, "r": 460.8484802246094, "b": 290.5433654785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "6. Future Work & Conclusion", "text": "6. Future Work & Conclusion", "level": 1}, {"self_ref": "#/texts/353", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 308.86199951171875, "t": 279.10662841796875, "r": 545.1151733398438, "b": 138.69407653808594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 640]}], "orig": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \"SynthTabNet\" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets.", "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. 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Tgrnet: A table graph reconstruction network for table structure recognition. arXiv preprint arXiv:2106.10598 , 2021. 3", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/393", "parent": {"cref": "#/groups/8"}, "children": [], "label": "list_item", "prov": [{"page_no": 10, "bbox": {"l": 50.112030029296875, "t": 197.3985137939453, "r": 286.3634033203125, "b": 156.50555419921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 220]}], "orig": "[35] Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo. Image captioning with semantic attention. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 4651-4659, 2016. 4", "text": "[35] Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo. Image captioning with semantic attention. 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Image-based table recognition: Data, model,", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/396", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 292.6300048828125, "t": 57.867008209228516, "r": 302.59259033203125, "b": 48.960445404052734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/397", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 10, "bbox": {"l": 328.781005859375, "t": 716.1165161132812, "r": 545.1145629882812, "b": 675.2245483398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 192]}], "orig": "and evaluation. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Computer Vision ECCV 2020 , pages 564-580, Cham, 2020. Springer International Publishing. 2, 3, 7", "text": "and evaluation. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Computer Vision ECCV 2020 , pages 564-580, Cham, 2020. Springer International Publishing. 2, 3, 7", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/398", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 10, "bbox": {"l": 308.86199951171875, "t": 671.2855224609375, "r": 545.1133422851562, "b": 630.392578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 221]}], "orig": "[38] Xu Zhong, Jianbin Tang, and Antonio Jimeno Yepes. Publaynet: Largest dataset ever for document layout analysis. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1", "text": "[38] Xu Zhong, Jianbin Tang, and Antonio Jimeno Yepes. Publaynet: Largest dataset ever for document layout analysis. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/399", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 132.8419952392578, "t": 681.4251098632812, "r": 465.37591552734375, "b": 656.4699096679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 83]}], "orig": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "level": 1}, {"self_ref": "#/texts/400", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 630.839111328125, "r": 175.96437072753906, "b": 620.0913696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "1. Details on the datasets", "text": "1. Details on the datasets", "level": 1}, {"self_ref": "#/texts/401", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 611.0206909179688, "r": 150.364013671875, "b": 601.1686401367188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "1.1. Data preparation", "text": "1.1. Data preparation", "level": 1}, {"self_ref": "#/texts/402", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 592.0797119140625, "r": 286.3651428222656, "b": 403.8451843261719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 931]}], "orig": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length.", "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length."}, {"self_ref": "#/texts/403", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 400.5947265625, "r": 286.3651123046875, "b": 164.54029846191406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1149]}], "orig": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"self_ref": "#/texts/404", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 161.28985595703125, "r": 286.3649597167969, "b": 140.42730712890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset.", "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"self_ref": "#/texts/405", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 129.60986328125, "r": 153.60784912109375, "b": 119.7578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "1.2. Synthetic datasets", "text": "1.2. Synthetic datasets", "level": 1}, {"self_ref": "#/texts/406", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 110.66886901855469, "r": 286.36505126953125, "b": 77.852294921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 167]}], "orig": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-", "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"self_ref": "#/texts/407", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 629.3448486328125, "r": 545.1151123046875, "b": 584.572265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 221]}], "orig": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).", "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"self_ref": "#/texts/408", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 580.7648315429688, "r": 545.1150512695312, "b": 559.9032592773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 89]}], "orig": "The process of generating a synthetic dataset can be decomposed into the following steps:", "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"self_ref": "#/texts/409", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 556.0947875976562, "r": 545.1151123046875, "b": 475.45721435546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 373]}], "orig": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/410", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 471.6497802734375, "r": 545.1151733398438, "b": 343.19134521484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/411", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 339.3839111328125, "r": 545.1151733398438, "b": 294.61138916015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 195]}], "orig": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/412", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 290.803955078125, "r": 545.1152954101562, "b": 246.0314178466797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 218]}], "orig": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/413", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 242.22396850585938, "r": 545.1151733398438, "b": 185.4964141845703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 238]}], "orig": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/414", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 169.70941162109375, "r": 545.1087646484375, "b": 145.01368713378906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "2. Prediction post-processing for PDF documents", "text": "2. Prediction post-processing for PDF documents", "level": 1}, {"self_ref": "#/texts/415", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.8620300292969, "t": 134.57896423339844, "r": 545.1151733398438, "b": 77.85139465332031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 247]}], "orig": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:", "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"self_ref": "#/texts/416", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 292.63104248046875, "t": 57.86696243286133, "r": 302.5936279296875, "b": 48.96039962768555, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/417", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 626.4976196289062, "r": 545.1137084960938, "b": 605.6360473632812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 245]}], "orig": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"self_ref": "#/texts/418", "parent": {"cref": "#/pictures/11"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 53.345978, "t": 716.80847, "r": 59.327053, "b": 710.8598, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "b.", "text": "b."}, {"self_ref": "#/texts/419", "parent": {"cref": "#/pictures/11"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 448.37271, "t": 714.7460300000001, "r": 481.75916, "b": 708.79736, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Table Bank", "text": "Table Bank"}, {"self_ref": "#/texts/420", "parent": {"cref": "#/pictures/11"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 119.39108, "t": 714.68945, "r": 151.94641, "b": 708.74078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], 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650.28937, "r": 328.80933, "b": 645.33215, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Simple", "text": "Simple"}, {"self_ref": "#/texts/476", "parent": {"cref": "#/pictures/11"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 185.37759, "t": 650.28882, "r": 202.84102, "b": 645.3316, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Simple", "text": "Simple"}, {"self_ref": "#/texts/477", "parent": {"cref": "#/groups/11"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 61.569000244140625, "t": 581.068603515625, "r": 286.3651123046875, "b": 560.20703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "\u00b7 TableFormer output does not include the table cell content.", "text": "\u00b7 TableFormer output does not include the table cell content.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/478", "parent": {"cref": "#/groups/11"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 61.569000244140625, "t": 547.9285888671875, "r": 286.3651428222656, "b": 527.0670166015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "text": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/479", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 512.7965698242188, "r": 286.3651123046875, "b": 396.2931213378906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 545]}], "orig": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes.", "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"self_ref": "#/texts/480", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 508.6367492675781, "r": 545.1151123046875, "b": 404.08929443359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 471]}], "orig": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/481", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 392.9306640625, "r": 286.3649597167969, "b": 372.068115234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "Here is a step-by-step description of the prediction postprocessing:", "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"self_ref": "#/texts/482", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 368.7046813964844, "r": 286.3650817871094, "b": 335.8881530761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 173]}], "orig": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/483", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 332.52471923828125, "r": 286.36505126953125, "b": 287.7532043457031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 187]}], "orig": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/484", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 284.3897705078125, "r": 286.36492919921875, "b": 263.5272216796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones.", "text": "3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/485", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 260.164794921875, "r": 286.3651123046875, "b": 227.34722900390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 131]}], "orig": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/486", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 223.98377990722656, "r": 286.3650817871094, "b": 191.16722106933594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 169]}], "orig": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/487", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 220.66197204589844, "r": 545.1168823242188, "b": 187.8454132080078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 113]}], "orig": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row).", "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"self_ref": "#/texts/488", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 12, "bbox": {"l": 110.70498657226562, "t": 168.5640869140625, "r": 286.3623962402344, "b": 137.89439392089844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } - min { x$_{c}$ } (4)", "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } - min { x$_{c}$ } (4)"}, {"self_ref": "#/texts/489", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 124.6520767211914, "r": 286.36199951171875, "b": 103.07321166992188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 103]}], "orig": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point.", "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"self_ref": "#/texts/490", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 123.969970703125, "r": 545.114990234375, "b": 103.10841369628906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 107]}], "orig": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "text": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/491", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 99.70977783203125, "r": 286.3649597167969, "b": 78.84821319580078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 110]}], "orig": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/492", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 99.70997619628906, "r": 545.1151733398438, "b": 78.84840393066406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 118]}], "orig": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/493", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 184.44696044921875, "r": 545.1150512695312, "b": 163.58441162109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "text": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/494", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 160.18597412109375, "r": 545.1150512695312, "b": 127.3694076538086, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 117]}], "orig": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/495", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 400.6898498535156, "r": 545.1151733398438, "b": 332.00836181640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 311]}], "orig": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/496", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 328.6089172363281, "r": 545.1151733398438, "b": 224.06141662597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 503]}], "orig": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/497", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 308.86199951171875, "t": 581.0687866210938, "r": 545.1151733398438, "b": 536.2962036132812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 183]}], "orig": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.", "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"self_ref": "#/texts/498", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.86199951171875, "t": 532.8977661132812, "r": 545.114990234375, "b": 512.0361938476562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 91]}], "orig": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/499", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 292.6310729980469, "t": 57.86697006225586, "r": 302.5936584472656, "b": 48.96040725708008, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "12", "text": "12"}, {"self_ref": "#/texts/500", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 88.84658813476562, "b": 707.8850708007812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "phan cell.", "text": "phan cell."}, {"self_ref": "#/texts/501", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 704.8366088867188, "r": 286.3649597167969, "b": 683.9750366210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "9f. Otherwise create a new structural cell and match it wit the orphan cell.", "text": "9f. Otherwise create a new structural cell and match it wit the orphan cell."}, {"self_ref": "#/texts/502", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 680.8369140625, "r": 286.364990234375, "b": 660.2941284179688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "Aditional images with examples of TableFormer predictions and post-processing can be found below.", "text": "Aditional images with examples of TableFormer predictions and post-processing can be found below."}, {"self_ref": "#/texts/503", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 63.340999603271484, "t": 289.9436340332031, "r": 273.1334228515625, "b": 281.0370788574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Figure 8: Example of a table with multi-line header.", "text": "Figure 8: Example of a table with multi-line header."}, {"self_ref": "#/texts/504", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 292.6309814453125, "t": 57.866641998291016, "r": 302.59356689453125, "b": 48.960079193115234, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "13", "text": "13"}, {"self_ref": "#/texts/505", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 308.86199951171875, "t": 485.4016418457031, "r": 545.1151123046875, "b": 464.54010009765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "Figure 9: Example of a table with big empty distance between cells.", "text": "Figure 9: Example of a table with big empty distance between cells."}, {"self_ref": "#/texts/506", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 312.3429870605469, "t": 111.50663757324219, "r": 541.63232421875, "b": 102.60006713867188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "Figure 10: Example of a complex table with empty cells.", "text": "Figure 10: Example of a complex table with empty cells."}, {"self_ref": "#/texts/507", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 50.11199951171875, "t": 435.2296447753906, "r": 286.3650817871094, "b": 414.36810302734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "Figure 11: Simple table with different style and empty cells.", "text": "Figure 11: Simple table with different style and empty cells."}, {"self_ref": "#/texts/508", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 54.61899948120117, "t": 120.181640625, "r": 281.85589599609375, "b": 111.27507781982422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "Figure 12: Simple table predictions and post processing.", "text": "Figure 12: Simple table predictions and post processing."}, {"self_ref": "#/texts/509", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 292.6309814453125, "t": 57.86663818359375, "r": 302.59356689453125, "b": 48.96007537841797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "14", "text": "14"}, {"self_ref": "#/texts/510", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 315.7900085449219, "t": 420.3156433105469, "r": 538.1852416992188, "b": 411.4090881347656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "Figure 13: Table predictions example on colorful table.", "text": "Figure 13: Table predictions example on colorful table."}, {"self_ref": "#/texts/511", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 344.9849853515625, "t": 108.45364379882812, "r": 508.9893493652344, "b": 99.54707336425781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Figure 14: Example with multi-line text.", "text": "Figure 14: Example with multi-line text."}, {"self_ref": "#/texts/512", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 84.23300170898438, "t": 147.64862060546875, "r": 252.24224853515625, "b": 138.7420654296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Figure 15: Example with triangular table.", "text": "Figure 15: Example with triangular table."}, {"self_ref": "#/texts/513", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 292.6309814453125, "t": 57.86665725708008, "r": 302.59356689453125, "b": 48.9600944519043, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15"}, {"self_ref": "#/texts/514", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 308.8619689941406, "t": 139.0646514892578, "r": 545.1151123046875, "b": 118.20308685302734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.", "text": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact."}, {"self_ref": "#/texts/515", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 16, "bbox": {"l": 50.11199951171875, "t": 283.6626281738281, "r": 545.1138305664062, "b": 262.80108642578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.", "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure."}, {"self_ref": "#/texts/516", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 292.6309814453125, "t": 57.866641998291016, "r": 302.59356689453125, "b": 48.960079193115234, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "16", "text": "16"}], "pictures": [{"self_ref": "#/pictures/0", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/8"}, {"cref": "#/texts/9"}, {"cref": "#/texts/10"}], "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 315.65362548828125, "t": 563.2765502929688, "r": 537.1475219726562, "b": 489.19854736328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [], "references": [], "footnotes": [], "image": null, "annotations": []}, {"self_ref": "#/pictures/1", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/13"}, {"cref": 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diff --git a/tests/data/groundtruth/docling_v2/2203.01017v2.md b/tests/data/groundtruth/docling_v2/2203.01017v2.md
index fef83d5e..fce10549 100644
--- a/tests/data/groundtruth/docling_v2/2203.01017v2.md
+++ b/tests/data/groundtruth/docling_v2/2203.01017v2.md
@@ -12,30 +12,26 @@
 
 The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.
 
-Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.
-
-|    | 3   |
-|----|-----|
-|  2 |     |
-
 <!-- image -->
 
+Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.
+
 - b. Red-annotation of bounding boxes, Blue-predictions by TableFormer
 
 <!-- image -->
 
 - c. Structure predicted by TableFormer:
 
-| 0      | 1 2   |   1 |
-|--------|-------|-----|
-| 3 4    | 5 3   |   6 |
-| 9      | 10    |  11 |
-| 8 13 2 | 14    |  15 |
-| 17     | 18    |  19 |
+<!-- image -->
 
 Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.
 
-<!-- image -->
+| 0   |   1 | 1   |   2 1 |   2 1 |    |
+|-----|-----|-----|-------|-------|----|
+| 3   |   4 | 5 3 |     6 |     7 |    |
+| 8   |   9 | 10  |    11 |    12 | 2  |
+|     |  13 | 14  |    15 |    16 | 2  |
+|     |  17 | 18  |    19 |    20 | 2  |
 
 Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.
 
diff --git a/tests/data/groundtruth/docling_v2/2203.01017v2.pages.json b/tests/data/groundtruth/docling_v2/2203.01017v2.pages.json
index 420017c7..66f05a36 100644
--- a/tests/data/groundtruth/docling_v2/2203.01017v2.pages.json
+++ b/tests/data/groundtruth/docling_v2/2203.01017v2.pages.json
@@ -1 +1 @@
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Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. 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This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 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Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). 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This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549326539039612, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": 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Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9791521430015564, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). 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This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph\u2019s, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF\u2019s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. 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"end_col_offset_idx": 3, "text": "19", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 459.87621999999993, "t": 437.5936, "r": 465.88101, "b": 447.724, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "1", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 308.862, "t": 514.50037, "r": 545.11517, "b": 559.2729, "coord_origin": "TOPLEFT"}, "confidence": 0.9214621186256409, "cells": [{"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: \u2018PMC2944238 004 02\u2019."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9791521430015564, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). 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This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph\u2019s, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF\u2019s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"label": "list_item", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549326539039612, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. 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multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: \u2018PMC2944238 004 02\u2019."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9791521430015564, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}], "headers": [{"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8773146271705627, "cells": [{"id": 125, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022"}, {"label": "page_footer", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.8045889139175415, "cells": [{"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656872153282166, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838618636131287, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428719282150269, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820107817649841, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822155237197876, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843322038650513, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.980295717716217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953584432601929, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778082728385925, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656872153282166, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838618636131287, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428719282150269, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820107817649841, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822155237197876, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843322038650513, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \u201cimage-encoder \u2192 text-decoder\u201d (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \u201cimage-encoder \u2192 dual decoder\u201d (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.980295717716217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953584432601929, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778082728385925, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}], "body": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656872153282166, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838618636131287, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428719282150269, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820107817649841, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822155237197876, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843322038650513, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \u201cimage-encoder \u2192 text-decoder\u201d (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \u201cimage-encoder \u2192 dual decoder\u201d (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.980295717716217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953584432601929, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://github.com/IBM/SynthTabNet"}], "headers": [{"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778082728385925, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0", "bbox": {"l": 320.97653, 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542.76428, "b": 161.21349999999995, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "4K", "bbox": {"l": 532.5705, "t": 176.75800000000004, "r": 542.53577, "b": 184.04796999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "2K", "bbox": {"l": 532.14551, "t": 199.6463, "r": 542.11078, "b": 206.93628, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767215609550476, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "picture", "bbox": {"l": 312.10369873046875, "t": 78.44087219238281, "r": 550.38916015625, "b": 250.60989379882812, 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396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 87.21935999999994, "r": 286.36514, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9822595119476318, "cells": [{"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 308.862, "t": 267.83636, "r": 545.11511, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9667503833770752, "cells": [{"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879812359809875, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840090274810791, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.87156081199646, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": 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"coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"label": "caption", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 308.862, "t": 267.83636, "r": 545.11511, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9667503833770752, "cells": [{"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN\u2019s) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN\u2019s) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879812359809875, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "balance in the previous datasets."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \u201csimple\u201d when it does not contain row spans or column spans, otherwise it is \u201ccomplex\u201d. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840090274810791, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}, {"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.87156081199646, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}], "body": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767215609550476, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tag-decoder which is constrained to the 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"both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"label": "caption", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 308.862, "t": 267.83636, "r": 545.11511, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9667503833770752, "cells": [{"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN\u2019s) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN\u2019s) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879812359809875, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "balance in the previous datasets."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \u201csimple\u201d when it does not contain row spans or column spans, otherwise it is \u201ccomplex\u201d. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840090274810791, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}], "headers": [{"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.87156081199646, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3", "bbox": {"l": 417.85599, "t": 85.6684600000001, "r": 425.37775, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "3", "bbox": {"l": 449.89569, "t": 85.6684600000001, "r": 457.41745000000003, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "509k", "bbox": {"l": 476.401, "t": 85.9673499999999, "r": 496.3262, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PNG", "bbox": {"l": 512.63495, "t": 85.9673499999999, "r": 532.56012, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "FinTabNet", "bbox": {"l": 317.06, "t": 97.92236000000003, "r": 359.43094, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "3", "bbox": {"l": 417.85599, "t": 97.62347, "r": 425.37775, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 449.89569, "t": 97.62347, "r": 457.41745000000003, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "112k", "bbox": {"l": 476.401, "t": 97.92236000000003, "r": 496.3262, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PDF", "bbox": {"l": 513.46185, "t": 97.92236000000003, "r": 531.73328, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "TableBank", "bbox": {"l": 317.06, "t": 109.87836000000004, "r": 359.97888, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "3", "bbox": {"l": 417.85599, "t": 109.57947000000001, "r": 425.37775, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "7", "bbox": {"l": 450.81226, "t": 109.57947000000001, "r": 456.50091999999995, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "145k", "bbox": {"l": 476.401, "t": 109.87836000000004, "r": 496.3262, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "JPEG", "bbox": {"l": 511.25017999999994, "t": 109.87836000000004, "r": 533.94501, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Combined-Tabnet(*)", "bbox": {"l": 317.06, "t": 121.83336999999995, "r": 400.37723, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3", "bbox": {"l": 417.85599, "t": 121.53448000000003, "r": 425.37775, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3", "bbox": {"l": 449.89569, "t": 121.53448000000003, "r": 457.41745000000003, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "400k", "bbox": {"l": 476.401, "t": 121.83336999999995, "r": 496.3262, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "PNG", "bbox": {"l": 512.63495, "t": 121.83336999999995, "r": 532.56012, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Combined(**)", "bbox": {"l": 317.06, "t": 133.78839000000005, "r": 375.17184, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "3", "bbox": {"l": 417.85599, "t": 133.48950000000002, "r": 425.37775, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3", "bbox": {"l": 449.89569, "t": 133.48950000000002, "r": 457.41745000000003, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "500k", "bbox": {"l": 476.401, "t": 133.78839000000005, "r": 496.3262, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "PNG", "bbox": {"l": 512.63495, "t": 133.78839000000005, "r": 532.56012, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "SynthTabNet", "bbox": {"l": 317.06, "t": 145.74341000000004, "r": 369.39352, "b": 154.64995999999996, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "3", "bbox": {"l": 417.85599, "t": 145.44446000000005, "r": 425.37775, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3", "bbox": {"l": 449.89569, "t": 145.44446000000005, "r": 457.41745000000003, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "600k", "bbox": {"l": 476.401, "t": 145.74334999999996, "r": 496.3262, "b": 154.6499, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "PNG", "bbox": {"l": 512.63495, "t": 145.74334999999996, "r": 532.56012, "b": 154.6499, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "table", "bbox": {"l": 317.06, "t": 73.61437999999998, "r": 536.91437, "b": 154.65985, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 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286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.66138, "r": 545.11505, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9668342471122742, "cells": [{"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9791285991668701, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9049099087715149, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874394536018372, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466320276260376, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.988465428352356, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376940131187439, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786656498908997, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"8": {"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 317.06, "t": 73.61437999999998, "r": 536.91437, "b": 154.65985, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, 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"b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9614067077636719, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.9880395531654358, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.66138, "r": 545.11505, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9668342471122742, "cells": [{"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: Both \u201cCombined-Tabnet\u201d and \u201dCombinedTabnet\u201d are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9791285991668701, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9049099087715149, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874394536018372, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466320276260376, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.988465428352356, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376940131187439, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (\u2018 < td > \u2019) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to \u2018 < \u2019, \u2018rowspan=\u2019 or \u2018colspan=\u2019, with the number of spanning cells (attribute), and \u2018 > \u2019. The hidden state attached to \u2018 < \u2019 is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786656498908997, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}, {"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}], "body": [{"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 317.06, "t": 73.61437999999998, "r": 536.91437, "b": 154.65985, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, 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false, "row_header": false, "row_section": false}, {"bbox": {"l": 449.89569, "t": 145.44446000000005, "r": 457.41745000000003, "b": 154.65985, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 476.401, "t": 145.74334999999996, "r": 496.3262, "b": 154.6499, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "600k", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 512.63495, "t": 145.74334999999996, "r": 532.56012, "b": 154.6499, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "PNG", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9614067077636719, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.9880395531654358, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.66138, "r": 545.11505, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9668342471122742, "cells": [{"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: Both \u201cCombined-Tabnet\u201d and \u201dCombinedTabnet\u201d are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9791285991668701, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9049099087715149, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874394536018372, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466320276260376, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.988465428352356, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376940131187439, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (\u2018 < td > \u2019) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to \u2018 < \u2019, \u2018rowspan=\u2019 or \u2018colspan=\u2019, with the number of spanning cells (attribute), and \u2018 > \u2019. The hidden state attached to \u2018 < \u2019 is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786656498908997, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}], "headers": [{"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "1.", "bbox": {"l": 81.688072, "t": 122.43970000000002, "r": 84.927567, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Item", "bbox": {"l": 86.54731, "t": 122.43970000000002, "r": 93.026291, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Amount", "bbox": {"l": 102.50498, "t": 115.25214000000005, "r": 115.3461, "b": 118.44135000000006, 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represent the input image.", "bbox": {"l": 50.112015, "t": 539.85738, "r": 286.36508, "b": 548.76393, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": 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Afterwards, a transformer en-", "bbox": {"l": 50.112015, "t": 587.6783800000001, "r": 286.36511, "b": 596.58493, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder and decoder architecture is employed to produce fea-", "bbox": {"l": 50.112015, "t": 599.63338, "r": 286.36508, "b": 608.53993, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "tures that are received by a linear layer, and the", "bbox": {"l": 50.112015, "t": 611.58838, "r": 240.43756000000002, "b": 620.4949300000001, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Cell BBox", "bbox": {"l": 243.19801, "t": 611.46883, "r": 286.36597, "b": 620.4252, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Decoder. The linear layer is applied to the features to", "bbox": {"l": 50.112015, "t": 623.42482, "r": 286.36511, "b": 632.3812, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "predict the tags. Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "forming classification, and adding an adaptive pooling", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 523.05786, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "layer", "bbox": {"l": 525.19983, "t": 249.53441999999995, "r": 545.11505, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "of size 28*28. 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318.32092, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "extensive experimentation, the", "bbox": {"l": 308.86194, "t": 321.36934999999994, "r": 432.35833999999994, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Structure Decoder", "bbox": {"l": 435.81995000000006, "t": 321.45901, "r": 510.29041, "b": 330.04678, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "is", "bbox": {"l": 513.97797, "t": 321.36934999999994, "r": 520.62305, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "mod-", "bbox": {"l": 524.08008, "t": 321.36934999999994, "r": 545.11115, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "eled as a transformer encoder with two encoder layers", "bbox": {"l": 308.86197, "t": 333.32434, "r": 527.76013, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "and", "bbox": {"l": 530.729, "t": 333.32434, "r": 545.11499, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "a transformer decoder made from a 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This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "picture", "bbox": {"l": 74.30538940429688, "t": 77.91117095947266, "r": 519.9801025390625, "b": 183.70108032226562, "coord_origin": "TOPLEFT"}, "confidence": 0.9296937584877014, "cells": [], "children": [{"id": 34, "label": "text", "bbox": {"l": 331.03699, "t": 78.55980999999997, "r": 352.12589, "b": 84.30042000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 24, "text": "BBoxes", "bbox": {"l": 331.03699, "t": 78.55980999999997, "r": 352.12589, "b": 84.30042000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 251.76939000000002, "t": 80.93096999999989, "r": 266.39557, "b": 86.67156999999997, "coord_origin": 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Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. 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The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 417.11426, "r": 545.11511, "b": 545.57271, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"id": 169, "text": "The transformer encoder receives an encoded", "bbox": {"l": 320.81696, "t": 417.11426, "r": 515.49609, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "image", "bbox": {"l": 520.7663, "t": 417.11426, "r": 545.11487, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "from the", "bbox": {"l": 308.86197, "t": 429.0692399999999, "r": 343.72107, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "CNN Backbone Network", "bbox": {"l": 347.03796, "t": 429.15891, "r": 446.45471000000003, "b": 437.74667, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "and refines it", "bbox": {"l": 449.93996999999996, "t": 429.0692399999999, "r": 503.06055000000003, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "through", "bbox": {"l": 506.37808, "t": 429.0692399999999, "r": 537.3717, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a", "bbox": {"l": 540.68927, "t": 429.0692399999999, "r": 545.11267, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "multi-head dot-product attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "caption", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 50.112, "t": 527.7828099999999, "r": 286.36597, "b": 680.27094, "coord_origin": "TOPLEFT"}, "confidence": 0.8913399577140808, "cells": [{"id": 107, "text": "Figure 4: Given an input image of a table, the", "bbox": {"l": 50.112, "t": 527.90237, "r": 229.78752, "b": 536.80893, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Encoder", "bbox": {"l": 231.787, "t": 527.7828099999999, "r": 267.76196, "b": 536.7392, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "pro-", "bbox": {"l": 269.76401, "t": 527.90237, "r": 286.36169, "b": 536.80893, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "duces fixed-length features that represent the input image.", "bbox": {"l": 50.112015, "t": 539.85738, "r": 286.36508, "b": 548.76393, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "Structure", "bbox": {"l": 245.59502, "t": 563.64882, "r": 286.362, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Decoder", "bbox": {"l": 50.112015, "t": 575.60382, "r": 85.519089, "b": 584.5602, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "receives \u2018tokenized tags\u2019 of the HTML code that", "bbox": {"l": 88.623016, "t": 575.7233699999999, "r": 286.36072, "b": 584.6299300000001, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "represent the table structure. Afterwards, a transformer en-", "bbox": {"l": 50.112015, "t": 587.6783800000001, "r": 286.36511, "b": 596.58493, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder and decoder architecture is employed to produce fea-", "bbox": {"l": 50.112015, "t": 599.63338, "r": 286.36508, "b": 608.53993, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "tures that are received by a linear layer, and the", "bbox": {"l": 50.112015, "t": 611.58838, "r": 240.43756000000002, "b": 620.4949300000001, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Cell BBox", "bbox": {"l": 243.19801, "t": 611.46883, "r": 286.36597, "b": 620.4252, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Decoder. The linear layer is applied to the features to", "bbox": {"l": 50.112015, "t": 623.42482, "r": 286.36511, "b": 632.3812, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "predict the tags. Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives \u2018tokenized tags\u2019 of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (\u2018 < td > \u2019, \u2018 < \u2019) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > \u2019 and \u2018 < \u2019 HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}, {"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}, "confidence": 0.8719567656517029, "cells": [{"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}], "body": [{"label": "picture", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "picture", "bbox": {"l": 74.30538940429688, "t": 77.91117095947266, "r": 519.9801025390625, "b": 183.70108032226562, "coord_origin": "TOPLEFT"}, "confidence": 0.9296937584877014, "cells": [], "children": [{"id": 34, "label": "text", "bbox": {"l": 331.03699, "t": 78.55980999999997, "r": 352.12589, "b": 84.30042000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 24, "text": "BBoxes", "bbox": {"l": 331.03699, "t": 78.55980999999997, "r": 352.12589, "b": 84.30042000000003, 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354.18588, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "ers that comprise mainly of multi-head attention and", "bbox": {"l": 308.86197, "t": 357.23532, "r": 524.51245, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "feed", "bbox": {"l": 527.96948, "t": 357.23532, "r": 545.11511, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "forward layers.", "bbox": {"l": 308.86197, "t": 369.19031000000007, "r": 370.39096, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "This configuration uses fewer layers", "bbox": {"l": 377.44449, "t": 369.19031000000007, "r": 526.91339, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "and", "bbox": {"l": 530.72906, "t": 369.19031000000007, "r": 545.11505, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "heads in comparison to networks applied to other", "bbox": {"l": 308.86197, "t": 381.14529000000005, "r": 505.46395999999993, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "problems", "bbox": {"l": 508.03430000000003, "t": 381.14529000000005, "r": 545.11511, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "(e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d),", "bbox": {"l": 308.86197, "t": 393.10028, "r": 517.68799, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "some-", "bbox": {"l": 520.76642, "t": 393.10028, "r": 545.11499, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "thing which we relate to the simplicity of table images.", "bbox": {"l": 308.86197, "t": 405.05526999999995, "r": 528.01935, "b": 413.96182, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 417.11426, "r": 545.11511, "b": 545.57271, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"id": 169, "text": "The transformer encoder receives an encoded", "bbox": {"l": 320.81696, "t": 417.11426, "r": 515.49609, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "image", "bbox": {"l": 520.7663, "t": 417.11426, "r": 545.11487, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "from the", "bbox": {"l": 308.86197, "t": 429.0692399999999, "r": 343.72107, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "CNN Backbone Network", "bbox": {"l": 347.03796, "t": 429.15891, "r": 446.45471000000003, "b": 437.74667, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "and refines it", "bbox": {"l": 449.93996999999996, "t": 429.0692399999999, "r": 503.06055000000003, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "through", "bbox": {"l": 506.37808, "t": 429.0692399999999, "r": 537.3717, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a", "bbox": {"l": 540.68927, "t": 429.0692399999999, "r": 545.11267, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "multi-head dot-product attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "caption", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 50.112, "t": 527.7828099999999, "r": 286.36597, "b": 680.27094, "coord_origin": "TOPLEFT"}, "confidence": 0.8913399577140808, "cells": [{"id": 107, "text": "Figure 4: Given an input image of a table, the", "bbox": {"l": 50.112, "t": 527.90237, "r": 229.78752, "b": 536.80893, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Encoder", "bbox": {"l": 231.787, "t": 527.7828099999999, "r": 267.76196, "b": 536.7392, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "pro-", "bbox": {"l": 269.76401, "t": 527.90237, "r": 286.36169, "b": 536.80893, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "duces fixed-length features that represent the input image.", "bbox": {"l": 50.112015, "t": 539.85738, "r": 286.36508, "b": 548.76393, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "Structure", "bbox": {"l": 245.59502, "t": 563.64882, "r": 286.362, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Decoder", "bbox": {"l": 50.112015, "t": 575.60382, "r": 85.519089, "b": 584.5602, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "receives \u2018tokenized tags\u2019 of the HTML code that", "bbox": {"l": 88.623016, "t": 575.7233699999999, "r": 286.36072, "b": 584.6299300000001, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "represent the table structure. Afterwards, a transformer en-", "bbox": {"l": 50.112015, "t": 587.6783800000001, "r": 286.36511, "b": 596.58493, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder and decoder architecture is employed to produce fea-", "bbox": {"l": 50.112015, "t": 599.63338, "r": 286.36508, "b": 608.53993, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "tures that are received by a linear layer, and the", "bbox": {"l": 50.112015, "t": 611.58838, "r": 240.43756000000002, "b": 620.4949300000001, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Cell BBox", "bbox": {"l": 243.19801, "t": 611.46883, "r": 286.36597, "b": 620.4252, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Decoder. The linear layer is applied to the features to", "bbox": {"l": 50.112015, "t": 623.42482, "r": 286.36511, "b": 632.3812, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "predict the tags. Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives \u2018tokenized tags\u2019 of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (\u2018 < td > \u2019, \u2018 < \u2019) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > \u2019 and \u2018 < \u2019 HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}], "headers": [{"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}, "confidence": 0.8719567656517029, "cells": [{"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423347115516663, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, 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"text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880596995353699, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441831469535828, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Transformer Encoder consists of two \u201cTransformer Encoder Layers\u201d, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \u201cTransformer Decoder Layers\u201d with similar input and output dimensions as the \u201cTransformer Encoder Layers\u201d. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a \u2019caching\u2019 technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423347115516663, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 \u2212 \u03bb ) l$_{box}$ (1)"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880596995353699, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441831469535828, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}], "body": [{"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Transformer Encoder consists of two \u201cTransformer Encoder Layers\u201d, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \u201cTransformer Decoder Layers\u201d with similar input and output dimensions as the \u201cTransformer Encoder Layers\u201d. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a \u2019caching\u2019 technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423347115516663, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 \u2212 \u03bb ) l$_{box}$ (1)"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880596995353699, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441831469535828, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}], "headers": [{"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 247.74349999999998, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "All", "bbox": {"l": 264.54044, "t": 426.66736, "r": 277.27264, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "EDD", "bbox": {"l": 81.612, "t": 443.62436, "r": 102.08514, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "PTN", "bbox": {"l": 134.87206, "t": 443.62436, "r": 153.69141, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "91.1", "bbox": {"l": 176.56554, "t": 443.62436, "r": 194.00009, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "88.7", "bbox": {"l": 220.82938000000001, "t": 443.62436, "r": 238.26393, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "89.9", "bbox": {"l": 262.18414, "t": 443.62436, "r": 279.61868, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "GTE", "bbox": {"l": 82.165001, "t": 455.58035, "r": 101.5323, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PTN", "bbox": {"l": 134.86716, "t": 455.58035, "r": 153.68651, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "-", "bbox": {"l": 183.62411, "t": 455.58035, "r": 186.94167, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "-", "bbox": {"l": 227.88795000000002, "t": 455.58035, "r": 231.20551, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "93.01", "bbox": {"l": 259.69855, "t": 455.58035, "r": 282.11441, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 468.13336, "r": 117.38329000000002, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PTN", "bbox": {"l": 134.86766, "t": 468.13336, "r": 153.68701, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "98.5", "bbox": {"l": 176.57111, "t": 468.13336, "r": 194.00566, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "95.0", "bbox": {"l": 220.83495, "t": 468.13336, "r": 238.26950000000002, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "96.75", "bbox": {"l": 259.698, "t": 468.01379, "r": 282.11386, "b": 476.97018, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "EDD", "bbox": {"l": 81.612, "t": 483.32635, "r": 102.08514, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "FTN", "bbox": {"l": 134.87206, "t": 483.32635, "r": 153.69141, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "88.4", "bbox": {"l": 176.56554, "t": 483.32635, "r": 194.00009, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "92.08", "bbox": {"l": 218.33870999999996, "t": 483.32635, "r": 240.75455999999997, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "90.6", "bbox": {"l": 262.18411, "t": 483.32635, "r": 279.61865, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "GTE", "bbox": {"l": 82.165001, "t": 495.28134, "r": 101.5323, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "FTN", "bbox": {"l": 134.86716, "t": 495.28134, "r": 153.68651, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "-", "bbox": {"l": 183.62411, "t": 495.28134, "r": 186.94167, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "-", "bbox": {"l": 227.88795000000002, "t": 495.28134, "r": 231.20551, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "87.14", "bbox": {"l": 259.69855, "t": 495.28134, "r": 282.11441, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "GTE (FT)", "bbox": {"l": 71.789001, "t": 507.23633, "r": 111.90838999999998, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "FTN", "bbox": {"l": 134.86221, "t": 507.23633, "r": 153.68156, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "-", "bbox": {"l": 183.62914, "t": 507.23633, "r": 186.94669, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "-", "bbox": {"l": 227.89297, "t": 507.23633, "r": 231.21053000000003, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "91.02", "bbox": {"l": 259.6936, "t": 507.23633, "r": 282.10947, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 519.1913099999999, "r": 117.38329000000002, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "FTN", "bbox": {"l": 134.86766, "t": 519.1913099999999, "r": 153.68701, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "97.5", "bbox": {"l": 176.57111, "t": 519.1913099999999, "r": 194.00566, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "96.0", "bbox": {"l": 220.83495, "t": 519.1913099999999, "r": 238.26950000000002, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "96.8", "bbox": {"l": 262.189, "t": 519.0717500000001, "r": 279.62354, "b": 528.02814, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "EDD", "bbox": {"l": 81.612, "t": 536.49837, "r": 102.08514, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "TB", "bbox": {"l": 137.91064, "t": 536.49837, "r": 150.64285, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "86.0", "bbox": {"l": 176.56554, "t": 536.49837, "r": 194.00009, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "-", "bbox": {"l": 227.89285, "t": 536.49837, "r": 231.21040000000002, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "86.0", "bbox": {"l": 262.18411, "t": 536.49837, "r": 279.61865, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 548.45436, "r": 117.38329000000002, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "TB", "bbox": {"l": 137.90625, "t": 548.45436, "r": 150.63846, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "89.6", "bbox": {"l": 176.57111, "t": 548.45436, "r": 194.00566, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "-", "bbox": {"l": 227.88845999999998, "t": 548.45436, "r": 231.20601, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "89.6", "bbox": {"l": 262.189, "t": 548.3348100000001, "r": 279.62354, "b": 557.2911799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 568.00237, "r": 117.38329000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "STN", "bbox": {"l": 134.86766, "t": 568.00237, "r": 153.68701, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "96.9", "bbox": {"l": 176.57111, "t": 568.00237, "r": 194.00566, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "95.7", "bbox": {"l": 220.83495, "t": 568.00237, "r": 238.26950000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "96.7", "bbox": {"l": 262.1897, "t": 568.00237, "r": 279.62424, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 270.62134000000003, "r": 377.00076, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "PubTabNet", "bbox": {"l": 393.69809, "t": 270.62134000000003, "r": 438.28073, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "79.2", "bbox": {"l": 455.63559, "t": 270.62134000000003, "r": 473.07013, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "82.7", "bbox": {"l": 498.16592, "t": 270.62134000000003, "r": 515.60046, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 282.57631999999995, "r": 377.86331, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "PubTabNet", "bbox": {"l": 393.69388, "t": 282.57631999999995, "r": 438.27652, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "82.1", "bbox": {"l": 455.63101, "t": 282.45676, "r": 473.06555000000003, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "86.8", "bbox": {"l": 498.1713, "t": 282.45676, "r": 515.60583, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 294.53131, "r": 377.86331, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "SynthTabNet", "bbox": {"l": 389.81842, "t": 294.53131, "r": 442.15194999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "87.7", "bbox": {"l": 455.63135, "t": 294.53131, "r": 473.06589, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "-", "bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "Table 3:", "bbox": {"l": 308.862, "t": 316.44931, "r": 341.49951, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Cell Bounding Box detection results on PubTab-", "bbox": {"l": 348.60284, "t": 316.44931, "r": 545.11517, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "Net, and FinTabNet. PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Model", "bbox": {"l": 358.01099, "t": 552.23337, "r": 384.02335, "b": 561.1399200000001, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "TEDS", "bbox": {"l": 449.03400000000005, "t": 546.25537, "r": 473.94049000000007, "b": 555.16193, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "Simple", "bbox": {"l": 408.50598, "t": 558.21037, "r": 436.73999, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Complex", "bbox": {"l": 448.6951, "t": 558.21037, "r": 485.07849, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "All", "bbox": {"l": 499.3848, "t": 558.21037, "r": 512.117, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "Tabula", "bbox": {"l": 357.68201, "t": 575.16736, "r": 384.3519, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "78.0", "bbox": {"l": 413.90097, "t": 575.16736, "r": 431.33550999999994, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "57.8", "bbox": {"l": 458.16479000000004, "t": 575.16736, "r": 475.59933000000007, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "67.9", "bbox": {"l": 497.0289, "t": 575.16736, "r": 514.46344, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "Traprange", "bbox": {"l": 350.72299, "t": 587.12236, "r": 391.31064, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "60.8", "bbox": {"l": 413.90582, "t": 587.12236, "r": 431.34036, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "49.9", "bbox": {"l": 458.16965, "t": 587.12236, "r": 475.60419, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "55.4", "bbox": {"l": 497.03374999999994, "t": 587.12236, "r": 514.46832, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Camelot", "bbox": {"l": 354.13599, "t": 599.07835, "r": 387.89923, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "80.0", "bbox": {"l": 413.90161, "t": 599.07835, "r": 431.33615, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "66.0", "bbox": {"l": 458.16544, "t": 599.07835, "r": 475.59998, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "73.0", "bbox": {"l": 497.02954000000005, "t": 599.07835, "r": 514.46411, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Acrobat Pro", "bbox": {"l": 346.55899, "t": 611.03336, "r": 395.47534, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "68.9", "bbox": {"l": 413.90616, "t": 611.03336, "r": 431.34069999999997, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "61.8", "bbox": {"l": 458.16998000000007, "t": 611.03336, "r": 475.60452, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "65.3", "bbox": {"l": 497.03409, "t": 611.03336, "r": 514.46863, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "EDD", "bbox": {"l": 360.78101, "t": 622.9883600000001, "r": 381.25415, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "91.2", "bbox": {"l": 413.90158, "t": 622.9883600000001, "r": 431.33612, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "85.4", "bbox": {"l": 458.16541, "t": 622.9883600000001, "r": 475.59995000000004, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "88.3", "bbox": {"l": 497.0295100000001, "t": 622.9883600000001, "r": 514.46405, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "TableFormer", "bbox": {"l": 345.483, "t": 634.94336, "r": 396.5513, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "95.4", "bbox": {"l": 413.90616, "t": 634.94336, "r": 431.34069999999997, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "90.1", "bbox": {"l": 458.16998000000007, "t": 634.94336, "r": 475.60452, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "93.6", "bbox": {"l": 497.03400000000005, "t": 634.82381, "r": 514.46857, "b": 643.78018, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "section_header", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}, "confidence": 0.9554283022880554, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500426650047302, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735332727432251, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855114221572876, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "table", "bbox": {"l": 326.79501, "t": 253.66436999999996, "r": 527.2276, "b": 303.43787, "coord_origin": "TOPLEFT"}, "confidence": 0.9691707491874695, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. 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Datasets and Metrics"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "our Cell BBox Decoder accuracy for cells with a class label of \u2018content\u2019 only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we\u2019ve integrated TableFormer\u2019s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"label": "formula", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500426650047302, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 \u2212 EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735332727432251, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855114221572876, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "table", "bbox": {"l": 326.79501, "t": 253.66436999999996, "r": 527.2276, "b": 303.43787, "coord_origin": "TOPLEFT"}, "confidence": 0.9691707491874695, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 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"start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "87.7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "-", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 308.862, "t": 316.44931, "r": 545.11517, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9519907236099243, "cells": [{"id": 162, "text": "Table 3:", "bbox": {"l": 308.862, "t": 316.44931, "r": 341.49951, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Cell Bounding Box detection results on PubTab-", "bbox": {"l": 348.60284, "t": 316.44931, "r": 545.11517, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "Net, and FinTabNet. PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 66.315002, "t": 414.71237, "r": 282.11441, "b": 576.90892, "coord_origin": "TOPLEFT"}, "confidence": 0.989250659942627, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 247.74349999999998, "b": 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"end_row_offset_idx": 7, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "93.6", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 15, "page_no": 6, "cluster": {"id": 15, "label": "text", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 613.29492, "coord_origin": "TOPLEFT"}, "confidence": 0.7209141850471497, "cells": [{"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"label": "text", "id": 16, "page_no": 6, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433366537094116, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "FT: Model was trained on PubTabNet then finetuned."}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854632616043091, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "caption", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 308.862, "t": 656.86136, "r": 545.11517, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9541405439376831, "cells": [{"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}, {"label": "page_footer", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}, "confidence": 0.8787976503372192, "cells": [{"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}], "body": [{"label": "section_header", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}, "confidence": 0.9554283022880554, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.3. Datasets and Metrics"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "our Cell BBox Decoder accuracy for cells with a class label of \u2018content\u2019 only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we\u2019ve integrated TableFormer\u2019s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"label": "formula", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500426650047302, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 \u2212 EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735332727432251, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855114221572876, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "table", "bbox": {"l": 326.79501, "t": 253.66436999999996, "r": 527.2276, "b": 303.43787, "coord_origin": "TOPLEFT"}, "confidence": 0.9691707491874695, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 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"bbox": {"l": 498.1713, "t": 282.45676, "r": 515.60583, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 294.53131, "r": 377.86331, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "SynthTabNet", "bbox": {"l": 389.81842, "t": 294.53131, "r": 442.15194999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "87.7", "bbox": {"l": 455.63135, "t": 294.53131, "r": 473.06589, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "-", "bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}], "children": [{"id": 73, "label": "text", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 74, "label": "text", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 75, "label": "text", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 76, "label": "text", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 77, 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 66.315002, "t": 414.71237, "r": 282.11441, "b": 576.90892, "coord_origin": "TOPLEFT"}, "confidence": 0.989250659942627, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 247.74349999999998, "b": 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"b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433366537094116, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "FT: Model was trained on PubTabNet then finetuned."}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854632616043091, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "caption", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 308.862, "t": 656.86136, "r": 545.11517, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9541405439376831, "cells": [{"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}], "headers": [{"label": "page_footer", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}, "confidence": 0.8787976503372192, "cells": [{"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "b.", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 62.219952, "b": 216.10645, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Structure predicted by TableFormer, with superimposed matched PDF cell text:", "bbox": {"l": 66.424026, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Japanese language (previously unseen by TableFormer):", "bbox": {"l": 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"TOPLEFT"}}, {"id": 180, "text": "Predicted Structure", "bbox": {"l": 384.35437, "t": 381.77722, "r": 430.99261, "b": 386.44281, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "bbox": {"l": 328.78101, "t": 704.920792, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "8", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 20, "label": "list_item", "bbox": {"l": 53.286037, "t": 78.68756000000008, "r": 61.550289, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.5819774866104126, "cells": [{"id": 4, "text": "a.", "bbox": {"l": 53.286037, "t": 78.68756000000008, "r": 61.550289, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "list_item", "bbox": {"l": 65.682419, "t": 78.68756000000008, "r": 499.55563, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.5412202477455139, "cells": [{"id": 5, "text": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "bbox": {"l": 65.682419, "t": 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This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852355718612671, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. 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"coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 162, "text": "53", "bbox": {"l": 528.04962, "t": 432.04431, "r": 534.3689, "b": 438.36295, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}, "confidence": 0.9153732657432556, "cells": [{"id": 181, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table."}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436547756195068, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}, "confidence": 0.9561256170272827, "cells": [{"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.5. Qualitative Analysis"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875592589378357, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \u201cSynthTabNet\u201d a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852355718612671, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various \u201ccomplex\u201d tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}, "confidence": 0.9442476034164429, "cells": [{"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}, "confidence": 0.8318724036216736, "cells": [{"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "bbox": {"l": 328.78101, "t": 704.920792, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-"}, {"label": "page_footer", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.87098228931427, "cells": [{"id": 220, "text": "8", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8"}], "body": [{"label": "list_item", "id": 20, "page_no": 7, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 53.286037, "t": 78.68756000000008, "r": 61.550289, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.5819774866104126, "cells": [{"id": 4, "text": "a.", "bbox": {"l": 53.286037, "t": 78.68756000000008, "r": 61.550289, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a."}, {"label": "list_item", "id": 21, "page_no": 7, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 65.682419, "t": 78.68756000000008, "r": 499.55563, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.5412202477455139, "cells": [{"id": 5, "text": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "bbox": {"l": 65.682419, "t": 78.68756000000008, "r": 499.55563, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells"}, {"label": "section_header", "id": 29, "page_no": 7, "cluster": {"id": 29, "label": "section_header", "bbox": {"l": 53.811783000000005, "t": 94.28112999999996, "r": 284.34592, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.4644920825958252, "cells": [{"id": 2, "text": "Japanese language (previously unseen by TableFormer):", "bbox": {"l": 53.811783000000005, "t": 94.28112999999996, "r": 284.34592, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Japanese language (previously unseen by TableFormer):"}, {"label": "section_header", "id": 31, "page_no": 7, "cluster": {"id": 31, "label": "section_header", "bbox": {"l": 304.83081, "t": 94.28112999999996, "r": 431.09119, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.46289125084877014, "cells": [{"id": 3, "text": "Example table from FinTabNet:", "bbox": {"l": 304.83081, "t": 94.28112999999996, "r": 431.09119, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example table from FinTabNet:"}, {"label": "picture", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "picture", "bbox": {"l": 305.5844421386719, "t": 98.65103149414062, "r": 554.8255615234375, "b": 180.62570190429688, "coord_origin": "TOPLEFT"}, "confidence": 0.7697681784629822, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "picture", "bbox": {"l": 49.97501754760742, "t": 103.71235656738281, "r": 301.6349182128906, "b": 187.57875061035156, "coord_origin": "TOPLEFT"}, "confidence": 0.7873176336288452, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "caption", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}, "confidence": 0.5986899733543396, "cells": [{"id": 0, "text": "b.", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 62.219952, "b": 216.10645, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Structure predicted by TableFormer, with superimposed matched PDF cell text:", "bbox": {"l": 66.424026, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. 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"section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436547756195068, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}, "confidence": 0.9561256170272827, "cells": [{"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.5. Qualitative Analysis"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875592589378357, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \u201cSynthTabNet\u201d a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852355718612671, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various \u201ccomplex\u201d tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}, "confidence": 0.9442476034164429, "cells": [{"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}, "confidence": 0.8318724036216736, "cells": [{"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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In", "bbox": {"l": 70.031013, "t": 517.15948, "r": 265.62408, "b": 525.17545, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "2019", "bbox": {"l": 268.42902, "t": 517.24017, "r": 286.36182, "b": 524.96924, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "International Conference on Document Analysis and Recog-", "bbox": {"l": 70.031021, "t": 528.19916, "r": 286.36337, "b": 535.92822, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "nition (ICDAR)", "bbox": {"l": 70.031021, "t": 539.15718, "r": 125.25507999999999, "b": 546.88622, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ", pages 749-755. IEEE, 2019. 3", "bbox": {"l": 125.25402, "t": 539.07648, "r": 240.05083, "b": 547.09244, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[33] Wenyuan Xue, Qingyong Li, and Dacheng Tao. Res2tim: reconstruct syntactic structures from table images. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 749-755. 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Image-based table recognition: Data, model,"}], "headers": [{"label": "page_footer", "id": 0, "page_no": 9, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 292.63, "t": 734.1329920000001, "r": 302.59259, "b": 743.039555, "coord_origin": "TOPLEFT"}, "confidence": 0.9069585204124451, "cells": [{"id": 127, "text": "10", "bbox": {"l": 292.63, "t": 734.1329920000001, "r": 302.59259, "b": 743.039555, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}]}}, {"page_no": 10, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497079730033875, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596062302589417, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699996113777161, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568929672241211, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335852265357971, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.978333592414856, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986662030220032, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497079730033875, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Details on the datasets"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596062302589417, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699996113777161, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568929672241211, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335852265357971, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.978333592414856, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986662030220032, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}], "body": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497079730033875, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Details on the datasets"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596062302589417, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699996113777161, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568929672241211, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335852265357971, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.978333592414856, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}], "headers": [{"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986662030220032, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "FinTabNet", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table Bank", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Train", "bbox": {"l": 82.553436, "t": 141.27617999999995, "r": 94.976013, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Complex", "bbox": {"l": 63.03878399999999, "t": 101.10413000000005, "r": 85.290085, "b": 106.06133999999986, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Simple", "bbox": {"l": 67.76786, "t": 124.39531999999997, "r": 85.231277, "b": 129.35253999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Complex", "bbox": {"l": 227.55121, "t": 102.53992000000005, "r": 249.80251, "b": 107.49712999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Simple", "bbox": {"l": 232.19898999999998, "t": 126.98577999999986, "r": 249.66241, "b": 131.94299, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Simple", "bbox": {"l": 396.2337, "t": 114.04522999999995, "r": 413.69711, "b": 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"Train Test Val", "bbox": {"l": 410.19409, "t": 141.27617999999995, "r": 444.68915, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "100% 130K 5K", "bbox": {"l": 391.37341, "t": 85.73321999999996, "r": 432.6716599999999, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "10K", "bbox": {"l": 435.60571000000004, "t": 86.26140999999996, "r": 445.62414999999993, "b": 91.21862999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Complex", "bbox": {"l": 113.94921, "t": 141.28845, "r": 136.20052, "b": 146.24567000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Non", "bbox": {"l": 116.91554000000001, "t": 94.81853999999998, "r": 127.05433999999998, "b": 99.77575999999999, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Strict", "bbox": {"l": 113.3146, "t": 100.93853999999999, "r": 127.05298, "b": 105.89575000000002, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "HTML", "bbox": {"l": 112.94112, "t": 107.05853000000013, "r": 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93.07977000000005, "r": 299.37451, "b": 98.03698999999995, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Strict", "bbox": {"l": 285.63513, "t": 99.19976999999994, "r": 299.3735, "b": 104.15698000000009, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "HTML", "bbox": {"l": 285.26111, "t": 105.31975999999997, "r": 299.37537, "b": 110.27697999999998, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Strict", "bbox": {"l": 285.43109, "t": 120.38995, "r": 299.16946, "b": 125.34717, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HTML", "bbox": {"l": 285.05713, "t": 126.50995, "r": 299.17139, "b": 131.46716000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Simple", "bbox": {"l": 311.34592, "t": 141.71063000000004, "r": 328.80933, "b": 146.66785000000004, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "47K", "bbox": {"l": 299.58362, "t": 86.69353999999998, "r": 309.60205, "b": 91.65075999999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Simple", "bbox": {"l": 466.04077000000007, "t": 141.67169, "r": 483.50418, "b": 146.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Non", "bbox": {"l": 459.02151, "t": 93.76116999999999, "r": 469.16031000000004, "b": 98.71838000000002, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Strict", "bbox": {"l": 455.4209, "t": 99.88116000000002, "r": 469.15927000000005, "b": 104.83838000000003, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "HTML", "bbox": {"l": 455.04691, "t": 106.00116000000014, "r": 469.16115999999994, "b": 110.95836999999995, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "145K", "bbox": {"l": 467.39401, "t": 85.57239000000004, "r": 480.6545100000001, "b": 90.52959999999996, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Complex", "bbox": {"l": 160.37672, "t": 141.58385999999996, "r": 182.62802, "b": 146.54107999999997, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Contain", "bbox": {"l": 153.74265, "t": 94.86481000000003, "r": 173.32664, "b": 99.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Missing", "bbox": {"l": 154.50967, "t": 100.98479999999995, "r": 173.3246, "b": 105.94202000000007, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "bboxes", "bbox": {"l": 155.27162, "t": 107.10479999999995, "r": 173.32664, "b": 112.06200999999987, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Contain", "bbox": {"l": 326.41302, "t": 107.23248000000001, "r": 345.99701, "b": 112.18970000000002, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Missing", "bbox": {"l": 327.17972, "t": 113.35248000000001, "r": 345.99463, "b": 118.30969000000005, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "bboxes", "bbox": {"l": 327.94131, "t": 119.47247000000004, "r": 345.99634, "b": 124.42969000000005, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Dataset", "bbox": {"l": 488.9942, "t": 104.15374999999983, "r": 508.76384999999993, "b": 109.11095999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "doesn't", "bbox": {"l": 490.1893, "t": 110.27373999999998, "r": 508.76349000000005, "b": 115.2309600000001, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "provide", "bbox": {"l": 489.72009, "t": 116.39373999999998, "r": 508.76758, "b": 121.35095000000013, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "bboxes", "bbox": {"l": 490.71121, "t": 122.51373000000001, "r": 508.76624, "b": 127.47095000000002, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Simple", "bbox": {"l": 185.37759, "t": 141.71118, "r": 202.84102, "b": 146.66840000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "230K 280K", "bbox": {"l": 168.50357, "t": 86.13611000000003, "r": 197.52699, "b": 91.09331999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "65K", "bbox": {"l": 357.3768, "t": 85.99707000000001, "r": 367.39523, "b": 90.95428000000004, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Complex Simple", "bbox": {"l": 333.73151, "t": 141.62323000000004, "r": 374.92862, "b": 146.58043999999995, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "47K", "bbox": {"l": 345.69101, "t": 86.05591000000004, "r": 355.70944, "b": 91.01312000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Simple", "bbox": {"l": 508.54248, "t": 141.37683000000004, "r": 526.00592, "b": 146.33405000000005, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "145K", "bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "9e. 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Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.8942293524742126, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.96295565366745, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423391819000244, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767084717750549, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562006592750549, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347665309906006, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280701637268066, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126085042953491, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491711854934692, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587164521217346, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459475636482239, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716898918151855, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298409819602966, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584902882575989, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.759074866771698, "cells": [{"id": 181, "text": "9d. 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Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"label": "text", "id": 16, "page_no": 11, "cluster": {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.8942293524742126, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.96295565366745, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423391819000244, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767084717750549, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562006592750549, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347665309906006, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280701637268066, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126085042953491, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as \u201cgood\u201d ones and \u201cbad\u201d ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491711854934692, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587164521217346, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459475636482239, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716898918151855, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9b. Intersect the orphan\u2019s bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298409819602966, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } \u2212 min { x$_{c}$ } (4)"}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584902882575989, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "text", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.759074866771698, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9d. Intersect the orphan\u2019s bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.290222, "r": 286.36496, "b": 713.151787, "coord_origin": "TOPLEFT"}, "confidence": 0.9260510802268982, "cells": [{"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}, {"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126599431037903, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}], "body": [{"label": "picture", "id": 30, "page_no": 11, "cluster": {"id": 30, "label": "picture", "bbox": {"l": 53.54228973388672, "t": 74.74851989746094, "r": 544.938232421875, "b": 147.5908966064453, "coord_origin": "TOPLEFT"}, "confidence": 0.6033812761306763, "cells": [], "children": [{"id": 27, "label": "text", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 3, "text": "Table Bank", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "FinTabNet", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 66, "label": "text", "bbox": {"l": 467.39401, "t": 85.57239000000004, "r": 480.6545100000001, "b": 90.52959999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 40, "text": "145K", "bbox": {"l": 467.39401, "t": 85.57239000000004, "r": 480.6545100000001, "b": 90.52959999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 60.93763400000001, "t": 85.73321999999996, "r": 76.151443, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "100%", "bbox": {"l": 60.93763400000001, "t": 85.73321999999996, "r": 76.151443, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 40, "label": "text", "bbox": {"l": 226.69780000000003, "t": 85.73321999999996, "r": 241.91161, "b": 90.69042999999999, 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The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.96295565366745, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423391819000244, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767084717750549, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562006592750549, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347665309906006, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280701637268066, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126085042953491, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as \u201cgood\u201d ones and \u201cbad\u201d ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491711854934692, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587164521217346, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459475636482239, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716898918151855, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9b. Intersect the orphan\u2019s bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298409819602966, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } \u2212 min { x$_{c}$ } (4)"}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584902882575989, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "text", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.759074866771698, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9d. Intersect the orphan\u2019s bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.290222, "r": 286.36496, "b": 713.151787, "coord_origin": "TOPLEFT"}, "confidence": 0.9260510802268982, "cells": [{"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}], "headers": [{"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126599431037903, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "phan cell.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 88.846588, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9f. Otherwise create a new structural cell and match it", "bbox": {"l": 62.067001, "t": 87.16339000000005, "r": 286.36496, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "wit the orphan cell.", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 127.03322, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Aditional images with examples of TableFormer predic-", "bbox": {"l": 62.067001, "t": 111.16309000000001, "r": 286.36499, "b": 119.7508499999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tions and post-processing can be found below.", "bbox": {"l": 50.112, "t": 123.11810000000003, "r": 234.06139999999996, "b": 131.70587, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Figure 8: Example of a table with multi-line header.", "bbox": {"l": 63.341, "t": 502.05637, "r": 273.13342, "b": 510.96292, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Figure 9:", "bbox": {"l": 308.862, "t": 306.59836, "r": 345.63397, "b": 315.50491, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Example of a table with big empty distance be-", "bbox": {"l": 352.78711, "t": 306.59836, "r": 545.11511, "b": 315.50491, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "tween cells.", "bbox": {"l": 308.862, "t": 318.55334, "r": 355.89545, "b": 327.45990000000006, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Figure 10: Example of a complex table with empty cells.", "bbox": {"l": 312.34299, "t": 680.4933599999999, "r": 541.63232, "b": 689.39993, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "13", "bbox": {"l": 292.63098, "t": 734.133358, "r": 302.59357, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 88.846588, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.7545604109764099, "cells": [{"id": 0, "text": "phan cell.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 88.846588, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36496, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9170765280723572, "cells": [{"id": 1, "text": "9f. Otherwise create a new structural cell and match it", "bbox": {"l": 62.067001, "t": 87.16339000000005, "r": 286.36496, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "wit the orphan cell.", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 127.03322, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "table", "bbox": {"l": 310.3294372558594, "t": 101.17761993408203, "r": 555.8338623046875, "b": 136.14747619628906, "coord_origin": "TOPLEFT"}, "confidence": 0.7048211097717285, "cells": [], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 111.16309000000001, "r": 286.36499, "b": 131.70587, "coord_origin": "TOPLEFT"}, "confidence": 0.9454684257507324, "cells": [{"id": 3, "text": "Aditional images with examples of TableFormer predic-", "bbox": {"l": 62.067001, "t": 111.16309000000001, "r": 286.36499, "b": 119.7508499999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tions and post-processing can be found below.", "bbox": {"l": 50.112, "t": 123.11810000000003, "r": 234.06139999999996, "b": 131.70587, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "table", "bbox": {"l": 309.9566345214844, "t": 154.61447143554688, "r": 555.7466430664062, "b": 184.72254943847656, "coord_origin": "TOPLEFT"}, "confidence": 0.5642898082733154, "cells": [], "children": []}, {"id": 9, "label": "table", "bbox": {"l": 84.0283203125, "t": 156.3335418701172, "r": 239.1690673828125, "b": 214.39334106445312, "coord_origin": "TOPLEFT"}, "confidence": 0.8857285976409912, "cells": [], "children": []}, {"id": 21, "label": "table", "bbox": {"l": 309.9635314941406, "t": 195.7053985595703, "r": 555.7054443359375, "b": 233.55148315429688, "coord_origin": "TOPLEFT"}, "confidence": 0.6602534651756287, "cells": [], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 82.92001342773438, "t": 233.7763214111328, "r": 239.1903533935547, "b": 291.283935546875, "coord_origin": "TOPLEFT"}, "confidence": 0.9155728220939636, "cells": [], "children": []}, {"id": 19, "label": "picture", "bbox": {"l": 309.79150390625, "t": 253.90536499023438, "r": 425.9603271484375, "b": 292.39398193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.6956613063812256, "cells": [], "children": []}, {"id": 37, "label": "table", "bbox": {"l": 309.79150390625, "t": 253.90536499023438, "r": 425.9603271484375, "b": 292.39398193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.5282703042030334, "cells": [], "children": []}, {"id": 11, "label": "caption", "bbox": {"l": 308.862, "t": 306.59836, "r": 545.11511, "b": 327.45990000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8773334622383118, "cells": [{"id": 6, "text": "Figure 9:", "bbox": {"l": 308.862, "t": 306.59836, "r": 345.63397, "b": 315.50491, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Example of a table with big empty distance be-", "bbox": {"l": 352.78711, "t": 306.59836, "r": 545.11511, "b": 315.50491, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "tween cells.", "bbox": {"l": 308.862, "t": 318.55334, "r": 355.89545, "b": 327.45990000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "table", "bbox": {"l": 83.94786071777344, "t": 309.0477294921875, "r": 239.17135620117188, "b": 367.9095764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9085132479667664, "cells": [], "children": []}, {"id": 12, "label": "table", "bbox": {"l": 335.2694091796875, "t": 388.46746826171875, "r": 490.08184814453125, "b": 437.02239990234375, "coord_origin": "TOPLEFT"}, "confidence": 0.8486074805259705, "cells": [], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 83.31758117675781, "t": 396.0135498046875, "r": 248.87306213378906, "b": 487.2569885253906, "coord_origin": "TOPLEFT"}, "confidence": 0.9613965749740601, "cells": [], "children": []}, {"id": 10, "label": "table", "bbox": {"l": 334.9334411621094, "t": 453.9476318359375, "r": 490.0914611816406, "b": 502.7210998535156, "coord_origin": "TOPLEFT"}, "confidence": 0.8815536499023438, "cells": [], "children": []}, {"id": 8, "label": "caption", "bbox": {"l": 63.341, "t": 502.05637, "r": 273.13342, "b": 510.96292, "coord_origin": "TOPLEFT"}, "confidence": 0.8971083164215088, "cells": [{"id": 5, "text": "Figure 8: Example of a table with multi-line header.", "bbox": {"l": 63.341, "t": 502.05637, "r": 273.13342, "b": 510.96292, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "table", "bbox": {"l": 335.2545471191406, "t": 519.07568359375, "r": 490.22369384765625, "b": 567.6879272460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9096733927726746, "cells": [], "children": []}, {"id": 13, "label": "picture", "bbox": {"l": 333.9573669433594, "t": 593.1134033203125, "r": 518.4768676757812, "b": 665.4903564453125, "coord_origin": "TOPLEFT"}, "confidence": 0.802356481552124, "cells": [], "children": []}, {"id": 30, "label": "table", "bbox": {"l": 333.9573669433594, "t": 593.1134033203125, "r": 518.4768676757812, "b": 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End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure."}, {"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 292.63098, "t": 734.133358, "r": 302.59357, "b": 743.039921, "coord_origin": "TOPLEFT"}, "confidence": 0.9134100675582886, "cells": [{"id": 2, "text": "16", "bbox": {"l": 292.63098, "t": 734.133358, "r": 302.59357, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "16"}], "body": [{"label": "picture", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 66.79946899414062, "t": 253.61631774902344, "r": 528.5564575195312, "b": 498.1383972167969, "coord_origin": "TOPLEFT"}, "confidence": 0.6913459897041321, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "caption", "bbox": {"l": 50.112, "t": 508.33737, "r": 545.11383, "b": 529.1989100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9624595642089844, "cells": [{"id": 0, "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post process-", "bbox": {"l": 50.112, "t": 508.33737, "r": 545.11383, "b": 517.24393, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "ing and prediction of structure.", "bbox": {"l": 50.112, "t": 520.2923599999999, "r": 173.23975, "b": 529.1989100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure."}], "headers": [{"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 292.63098, "t": 734.133358, "r": 302.59357, "b": 743.039921, "coord_origin": "TOPLEFT"}, "confidence": 0.9134100675582886, "cells": [{"id": 2, "text": "16", "bbox": {"l": 292.63098, "t": 734.133358, "r": 302.59357, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "16"}]}}]
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+[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers.", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 157.37334999999996, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "IBM Research", "bbox": {"l": 262.918, "t": 160.63239, "r": 332.30597, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Abstract", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tables organize valuable content in a concise and com-", "bbox": {"l": 62.066978, "t": 241.39508, "r": 286.36493, "b": 249.98284999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "pact representation. This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 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Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). 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This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549326539039612, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": 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Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9791521430015564, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). 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This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph\u2019s, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF\u2019s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. 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[{"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: \u2018PMC2944238 004 02\u2019."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9791521430015564, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}, {"label": "page_footer", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.8045889139175415, "cells": [{"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}], "body": [{"label": "section_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8868061304092407, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers.", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers."}, {"label": "section_header", "id": 13, "page_no": 0, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7586213946342468, "cells": [{"id": 1, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 157.37334999999996, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "IBM Research", "bbox": {"l": 262.918, "t": 160.63239, "r": 332.30597, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research"}, {"label": "key_value_region", "id": 25, "page_no": 0, "cluster": {"id": 25, "label": "key_value_region", "bbox": {"l": 208.60328674316406, "t": 175.79937744140625, "r": 379.33544921875, "b": 185.4495086669922, "coord_origin": "TOPLEFT"}, "confidence": 0.48547235131263733, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": [{"id": 7, "label": "text", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 378.73257, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9096333980560303, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9258671998977661, "cells": [{"id": 7, "text": "Abstract", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract"}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 315.56702, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}, "confidence": 0.6724023818969727, "cells": [{"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a. Picture of a table:"}, {"label": "picture", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.65362548828125, "t": 228.7234344482422, "r": 537.1475219726562, "b": 302.80145263671875, "coord_origin": "TOPLEFT"}, "confidence": 0.608779788017273, "cells": [], "children": [{"id": 62, "label": "text", "bbox": {"l": 451.9457100000001, "t": 235.34704999999997, "r": 457.95050000000003, "b": 245.47748, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 97, "text": "1", "bbox": {"l": 451.9457100000001, "t": 235.34704999999997, "r": 457.95050000000003, "b": 245.47748, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 70, "label": "text", "bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 105, "text": "3", "bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 65, "label": "text", "bbox": {"l": 331.19681, "t": 269.35266, "r": 337.2016, "b": 279.48308999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 100, "text": "2", "bbox": {"l": 331.19681, "t": 269.35266, "r": 337.2016, "b": 279.48308999999995, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "table", "id": 15, "page_no": 0, "cluster": {"id": 15, "label": "table", "bbox": {"l": 315.65362548828125, "t": 228.7234344482422, "r": 537.1475219726562, "b": 302.80145263671875, "coord_origin": "TOPLEFT"}, "confidence": 0.651587724685669, "cells": [{"id": 97, "text": "1", "bbox": {"l": 451.9457100000001, "t": 235.34704999999997, "r": 457.95050000000003, "b": 245.47748, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "2", "bbox": {"l": 331.19681, "t": 269.35266, "r": 337.2016, "b": 279.48308999999995, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3", "bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}}], "children": [{"id": 62, "label": "text", "bbox": {"l": 451.9457100000001, "t": 235.34704999999997, "r": 457.95050000000003, "b": 245.47748, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 97, "text": "1", "bbox": {"l": 451.9457100000001, "t": 235.34704999999997, "r": 457.95050000000003, "b": 245.47748, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 70, "label": "text", "bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 105, "text": "3", "bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 65, "label": "text", "bbox": {"l": 331.19681, "t": 269.35266, "r": 337.2016, "b": 279.48308999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 100, "text": "2", "bbox": {"l": 331.19681, "t": 269.35266, "r": 337.2016, "b": 279.48308999999995, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null, "otsl_seq": ["ecel", "ched", "ched", "ched", "ched", "nl", "rhed", "fcel", "fcel", "fcel", "fcel", "nl", "ucel", "fcel", "fcel", "fcel", "fcel", "nl", "ucel", "fcel", "fcel", "fcel", "fcel", "nl"], "num_rows": 1, "num_cols": 2, "table_cells": [{"bbox": {"l": 451.9457100000001, "t": 235.34704999999997, "r": 457.95050000000003, "b": 245.47748, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3", "column_header": true, "row_header": false, "row_section": false}]}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111977, "t": 241.39508, "r": 286.36511, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9838882088661194, "cells": [{"id": 8, "text": "Tables organize valuable content in a concise and com-", "bbox": {"l": 62.066978, "t": 241.39508, "r": 286.36493, "b": 249.98284999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "pact representation. This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph\u2019s, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF\u2019s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"label": "list_item", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549326539039612, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. 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[{"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: \u2018PMC2944238 004 02\u2019."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9791521430015564, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}], "headers": [{"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8773146271705627, "cells": [{"id": 125, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022"}, {"label": "page_footer", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.8045889139175415, "cells": [{"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656872153282166, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838618636131287, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428719282150269, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820107817649841, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822155237197876, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843322038650513, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.980295717716217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953584432601929, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778082728385925, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656872153282166, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838618636131287, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428719282150269, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820107817649841, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822155237197876, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843322038650513, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \u201cimage-encoder \u2192 text-decoder\u201d (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \u201cimage-encoder \u2192 dual decoder\u201d (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.980295717716217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953584432601929, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778082728385925, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}], "body": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656872153282166, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838618636131287, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428719282150269, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820107817649841, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822155237197876, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843322038650513, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \u201cimage-encoder \u2192 text-decoder\u201d (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \u201cimage-encoder \u2192 dual decoder\u201d (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.980295717716217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953584432601929, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://github.com/IBM/SynthTabNet"}], "headers": [{"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778082728385925, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0", "bbox": {"l": 320.97653, "t": 233.42296999999996, "r": 324.79254, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "20", "bbox": {"l": 410.483, "t": 233.42296999999996, "r": 418.11319, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "40", "bbox": {"l": 500.84949, "t": 233.42296999999996, "r": 508.47968000000003, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "10", "bbox": {"l": 365.29999, "t": 233.42296999999996, "r": 372.93018, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "30", "bbox": {"l": 455.66626, "t": 233.42296999999996, "r": 463.29645, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "50", "bbox": {"l": 542.03528, "t": 233.42296999999996, "r": 549.66547, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "0", "bbox": {"l": 316.04474, "t": 230.44617000000005, "r": 319.86075, "b": 236.27819999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "2", "bbox": {"l": 312.62521, "t": 198.69073000000003, "r": 316.44122, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "0", "bbox": {"l": 316.43942, "t": 198.69073000000003, "r": 320.2554, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "4", "bbox": {"l": 313.14951, "t": 168.09795999999994, "r": 316.96552, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "0", "bbox": {"l": 316.96371, "t": 168.09795999999994, "r": 320.77969, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "6", "bbox": {"l": 312.92972, "t": 136.58771000000002, "r": 316.74573, "b": 142.41974000000005, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "0", "bbox": {"l": 316.74393, "t": 136.58771000000002, "r": 320.55991, "b": 142.41974000000005, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "8", "bbox": {"l": 312.48227, "t": 105.60175000000004, "r": 316.29828, "b": 111.43377999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "0", "bbox": {"l": 316.29648, "t": 105.60175000000004, "r": 320.11246, "b": 111.43377999999996, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "1", "bbox": {"l": 312.48227, "t": 212.25922000000003, "r": 316.29828, "b": 218.09124999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "0", "bbox": {"l": 316.29648, "t": 212.25922000000003, "r": 320.11246, "b": 218.09124999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "3", "bbox": {"l": 313.07639, "t": 183.72198000000003, "r": 316.8924, "b": 189.55402000000004, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "0", "bbox": {"l": 316.89059, "t": 183.72198000000003, "r": 320.70657, "b": 189.55402000000004, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "5", "bbox": {"l": 312.76321, "t": 152.47400000000005, "r": 316.57922, "b": 158.30602999999996, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0", "bbox": {"l": 316.57742, "t": 152.47400000000005, "r": 320.3934, "b": 158.30602999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "7", "bbox": {"l": 312.19775, "t": 120.57050000000004, "r": 316.01376, "b": 126.40252999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0", "bbox": {"l": 316.01196, "t": 120.57050000000004, "r": 319.82794, "b": 126.40252999999996, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "9", "bbox": {"l": 312.8165, "t": 90.1087, "r": 316.63251, "b": 95.94073000000003, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "0", "bbox": {"l": 316.63071, "t": 90.1087, "r": 320.44669, "b": 95.94073000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "0", "bbox": {"l": 532.17426, "t": 222.72729000000004, "r": 536.94427, "b": 230.01727000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "10K", "bbox": {"l": 532.87952, "t": 108.26702999999986, "r": 547.61249, "b": 115.55700999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "8K", "bbox": {"l": 532.7735, "t": 130.78101000000004, "r": 542.73877, "b": 138.07097999999996, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "6K", "bbox": {"l": 532.79901, "t": 153.92352000000005, "r": 542.76428, "b": 161.21349999999995, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "4K", "bbox": {"l": 532.5705, "t": 176.75800000000004, "r": 542.53577, "b": 184.04796999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "2K", "bbox": {"l": 532.14551, "t": 199.6463, "r": 542.11078, "b": 206.93628, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767215609550476, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "picture", "bbox": {"l": 312.10369873046875, "t": 78.44087219238281, "r": 550.38916015625, "b": 250.60989379882812, 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For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 308.862, "t": 267.83636, "r": 545.11511, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9667503833770752, "cells": [{"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879812359809875, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840090274810791, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.87156081199646, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767215609550476, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tag-decoder which is constrained to the table-tags."}, {"label": "picture", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 312.10369873046875, "t": 78.44087219238281, "r": 550.38916015625, "b": 250.60989379882812, "coord_origin": "TOPLEFT"}, "confidence": 0.9746918082237244, "cells": [], "children": [{"id": 12, "label": "section_header", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, 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"coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"label": "caption", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 308.862, "t": 267.83636, "r": 545.11511, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9667503833770752, "cells": [{"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN\u2019s) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN\u2019s) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879812359809875, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "balance in the previous datasets."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \u201csimple\u201d when it does not contain row spans or column spans, otherwise it is \u201ccomplex\u201d. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840090274810791, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}, {"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.87156081199646, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}], "body": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767215609550476, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tag-decoder which is constrained to the table-tags."}, {"label": "picture", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 312.10369873046875, "t": 78.44087219238281, "r": 550.38916015625, "b": 250.60989379882812, "coord_origin": "TOPLEFT"}, "confidence": 0.9746918082237244, "cells": [], "children": [{"id": 12, "label": "section_header", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.5687219500541687, "cells": [{"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 312.8165, "t": 90.1087, "r": 316.63251, "b": 95.94073000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 90, "text": "9", "bbox": {"l": 312.8165, "t": 90.1087, "r": 316.63251, "b": 95.94073000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 38, 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"label": "text", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 87.21935999999994, "r": 286.36514, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9822595119476318, "cells": [{"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"label": "caption", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 308.862, "t": 267.83636, "r": 545.11511, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9667503833770752, "cells": [{"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN\u2019s) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN\u2019s) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879812359809875, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "balance in the previous datasets."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \u201csimple\u201d when it does not contain row spans or column spans, otherwise it is \u201ccomplex\u201d. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840090274810791, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}], "headers": [{"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.87156081199646, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3", "bbox": {"l": 417.85599, "t": 85.6684600000001, "r": 425.37775, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "3", "bbox": {"l": 449.89569, "t": 85.6684600000001, "r": 457.41745000000003, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "509k", "bbox": {"l": 476.401, "t": 85.9673499999999, "r": 496.3262, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PNG", "bbox": {"l": 512.63495, "t": 85.9673499999999, "r": 532.56012, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "FinTabNet", "bbox": {"l": 317.06, "t": 97.92236000000003, "r": 359.43094, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "3", "bbox": {"l": 417.85599, "t": 97.62347, "r": 425.37775, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 449.89569, "t": 97.62347, "r": 457.41745000000003, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "112k", "bbox": {"l": 476.401, "t": 97.92236000000003, "r": 496.3262, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PDF", "bbox": {"l": 513.46185, "t": 97.92236000000003, "r": 531.73328, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "TableBank", "bbox": {"l": 317.06, "t": 109.87836000000004, "r": 359.97888, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "3", "bbox": {"l": 417.85599, "t": 109.57947000000001, "r": 425.37775, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "7", "bbox": {"l": 450.81226, "t": 109.57947000000001, "r": 456.50091999999995, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "145k", "bbox": {"l": 476.401, "t": 109.87836000000004, "r": 496.3262, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "JPEG", "bbox": {"l": 511.25017999999994, "t": 109.87836000000004, "r": 533.94501, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Combined-Tabnet(*)", "bbox": {"l": 317.06, "t": 121.83336999999995, "r": 400.37723, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3", "bbox": {"l": 417.85599, "t": 121.53448000000003, "r": 425.37775, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3", "bbox": {"l": 449.89569, "t": 121.53448000000003, "r": 457.41745000000003, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "400k", "bbox": {"l": 476.401, "t": 121.83336999999995, "r": 496.3262, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "PNG", "bbox": {"l": 512.63495, "t": 121.83336999999995, "r": 532.56012, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Combined(**)", "bbox": {"l": 317.06, "t": 133.78839000000005, "r": 375.17184, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "3", "bbox": {"l": 417.85599, "t": 133.48950000000002, "r": 425.37775, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3", "bbox": {"l": 449.89569, "t": 133.48950000000002, "r": 457.41745000000003, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "500k", "bbox": {"l": 476.401, "t": 133.78839000000005, "r": 496.3262, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "PNG", "bbox": {"l": 512.63495, "t": 133.78839000000005, "r": 532.56012, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "SynthTabNet", "bbox": {"l": 317.06, "t": 145.74341000000004, "r": 369.39352, "b": 154.64995999999996, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "3", "bbox": {"l": 417.85599, "t": 145.44446000000005, "r": 425.37775, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3", "bbox": {"l": 449.89569, "t": 145.44446000000005, "r": 457.41745000000003, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "600k", "bbox": {"l": 476.401, "t": 145.74334999999996, "r": 496.3262, "b": 154.6499, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "PNG", "bbox": {"l": 512.63495, "t": 145.74334999999996, "r": 532.56012, "b": 154.6499, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. 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"r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.66138, "r": 545.11505, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9668342471122742, "cells": [{"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9791285991668701, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9049099087715149, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874394536018372, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466320276260376, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.988465428352356, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376940131187439, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786656498908997, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"8": {"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 310.6773681640625, "t": 73.19307708740234, "r": 542.958251953125, "b": 155.2208251953125, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": 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"id": 10, "page_no": 3, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9614067077636719, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.9880395531654358, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.66138, "r": 545.11505, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9668342471122742, "cells": [{"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: Both \u201cCombined-Tabnet\u201d and \u201dCombinedTabnet\u201d are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9791285991668701, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9049099087715149, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874394536018372, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466320276260376, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.988465428352356, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376940131187439, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (\u2018 < td > \u2019) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to \u2018 < \u2019, \u2018rowspan=\u2019 or \u2018colspan=\u2019, with the number of spanning cells (attribute), and \u2018 > \u2019. The hidden state attached to \u2018 < \u2019 is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786656498908997, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}, {"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}], "body": [{"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 310.6773681640625, "t": 73.19307708740234, "r": 542.958251953125, "b": 155.2208251953125, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, 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Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.66138, "r": 545.11505, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9668342471122742, "cells": [{"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: Both \u201cCombined-Tabnet\u201d and \u201dCombinedTabnet\u201d are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9791285991668701, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9049099087715149, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874394536018372, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466320276260376, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.988465428352356, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376940131187439, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (\u2018 < td > \u2019) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to \u2018 < \u2019, \u2018rowspan=\u2019 or \u2018colspan=\u2019, with the number of spanning cells (attribute), and \u2018 > \u2019. The hidden state attached to \u2018 < \u2019 is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786656498908997, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}], "headers": [{"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "1.", "bbox": {"l": 81.688072, "t": 122.43970000000002, "r": 84.927567, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Item", "bbox": {"l": 86.54731, "t": 122.43970000000002, "r": 93.026291, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Amount", "bbox": {"l": 102.50498, "t": 115.25214000000005, "r": 115.3461, "b": 118.44135000000006, 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"coord_origin": "TOPLEFT"}}, {"id": 30, "text": "provide full description of", "bbox": {"l": 431.1738, "t": 157.80517999999995, "r": 498.87753000000004, "b": 163.54578000000004, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "the table structure", "bbox": {"l": 440.5289, "t": 163.92516999999998, "r": 489.51827999999995, "b": 169.66576999999995, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Structure Tags", "bbox": {"l": 328.37479, "t": 178.25385000000006, "r": 367.72333, "b": 183.99445000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "BBoxes in sync", "bbox": {"l": 331.84451, "t": 123.90886999999998, "r": 373.67963, "b": 129.64948000000015, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "with tag sequence", "bbox": {"l": 331.84451, "t": 129.00885000000017, "r": 381.17786, "b": 134.74945000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Encoder", "bbox": {"l": 196.62633, "t": 88.11621000000002, "r": 219.42332, "b": 93.85681, "coord_origin": "TOPLEFT"}}, {"id": 36, 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The bounding boxes grabs the content from the PDF and inserts it in the structure.", "bbox": {"l": 50.111992, "t": 216.06035999999995, "r": 436.0134, "b": 224.96691999999996, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Input Image", "bbox": {"l": 74.253464, "t": 258.21472000000006, "r": 101.75846, "b": 264.17474000000004, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Tokenised Tags", "bbox": {"l": 122.29972, "t": 258.34520999999995, "r": 157.83972, "b": 264.30524, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Multi-Head Attention", "bbox": {"l": 78.549347, "t": 371.38579999999996, "r": 125.68359000000001, "b": 377.04782, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Add", "bbox": {"l": 78.513298, "t": 391.31857, "r": 84.644547, "b": 396.98059, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "& Normalisation", "bbox": {"l": 116.52705, "t": 391.31857, "r": 125.11079999999998, "b": 396.98059, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "Feed Forward Network", "bbox": 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represent the input image.", "bbox": {"l": 50.112015, "t": 539.85738, "r": 286.36508, "b": 548.76393, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": 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Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "forming classification, and adding an adaptive pooling", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 523.05786, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "layer", "bbox": {"l": 525.19983, "t": 249.53441999999995, "r": 545.11505, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "of size 28*28. 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This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. 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Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. 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The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 417.11426, "r": 545.11511, "b": 545.57271, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"id": 169, "text": "The transformer encoder receives an encoded", "bbox": {"l": 320.81696, "t": 417.11426, "r": 515.49609, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "image", "bbox": {"l": 520.7663, "t": 417.11426, "r": 545.11487, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "from the", "bbox": {"l": 308.86197, "t": 429.0692399999999, "r": 343.72107, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "CNN Backbone Network", "bbox": {"l": 347.03796, "t": 429.15891, "r": 446.45471000000003, "b": 437.74667, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "and refines it", "bbox": {"l": 449.93996999999996, "t": 429.0692399999999, "r": 503.06055000000003, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "through", "bbox": {"l": 506.37808, "t": 429.0692399999999, "r": 537.3717, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a", "bbox": {"l": 540.68927, "t": 429.0692399999999, "r": 545.11267, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "multi-head dot-product attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "caption", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 50.112, "t": 527.7828099999999, "r": 286.36597, "b": 680.27094, "coord_origin": "TOPLEFT"}, "confidence": 0.8913399577140808, "cells": [{"id": 107, "text": "Figure 4: Given an input image of a table, the", "bbox": {"l": 50.112, "t": 527.90237, "r": 229.78752, "b": 536.80893, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Encoder", "bbox": {"l": 231.787, "t": 527.7828099999999, "r": 267.76196, "b": 536.7392, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "pro-", "bbox": {"l": 269.76401, "t": 527.90237, "r": 286.36169, "b": 536.80893, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "duces fixed-length features that represent the input image.", "bbox": {"l": 50.112015, "t": 539.85738, "r": 286.36508, "b": 548.76393, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "Structure", "bbox": {"l": 245.59502, "t": 563.64882, "r": 286.362, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Decoder", "bbox": {"l": 50.112015, "t": 575.60382, "r": 85.519089, "b": 584.5602, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "receives \u2018tokenized tags\u2019 of the HTML code that", "bbox": {"l": 88.623016, "t": 575.7233699999999, "r": 286.36072, "b": 584.6299300000001, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "represent the table structure. Afterwards, a transformer en-", "bbox": {"l": 50.112015, "t": 587.6783800000001, "r": 286.36511, "b": 596.58493, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder and decoder architecture is employed to produce fea-", "bbox": {"l": 50.112015, "t": 599.63338, "r": 286.36508, "b": 608.53993, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "tures that are received by a linear layer, and the", "bbox": {"l": 50.112015, "t": 611.58838, "r": 240.43756000000002, "b": 620.4949300000001, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Cell BBox", "bbox": {"l": 243.19801, "t": 611.46883, "r": 286.36597, "b": 620.4252, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Decoder. The linear layer is applied to the features to", "bbox": {"l": 50.112015, "t": 623.42482, "r": 286.36511, "b": 632.3812, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "predict the tags. Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives \u2018tokenized tags\u2019 of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (\u2018 < td > \u2019, \u2018 < \u2019) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > \u2019 and \u2018 < \u2019 HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}, {"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}, "confidence": 0.8719567656517029, "cells": [{"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}], "body": [{"label": "picture", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "picture", "bbox": {"l": 74.30538940429688, "t": 77.91117095947266, "r": 519.9801025390625, "b": 183.70108032226562, "coord_origin": "TOPLEFT"}, "confidence": 0.9296937584877014, "cells": [], "children": [{"id": 34, "label": "text", "bbox": {"l": 331.03699, "t": 78.55980999999997, "r": 352.12589, "b": 84.30042000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 24, "text": "BBoxes", "bbox": {"l": 331.03699, "t": 78.55980999999997, "r": 352.12589, "b": 84.30042000000003, 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354.18588, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "ers that comprise mainly of multi-head attention and", "bbox": {"l": 308.86197, "t": 357.23532, "r": 524.51245, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "feed", "bbox": {"l": 527.96948, "t": 357.23532, "r": 545.11511, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "forward layers.", "bbox": {"l": 308.86197, "t": 369.19031000000007, "r": 370.39096, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "This configuration uses fewer layers", "bbox": {"l": 377.44449, "t": 369.19031000000007, "r": 526.91339, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "and", "bbox": {"l": 530.72906, "t": 369.19031000000007, "r": 545.11505, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "heads in comparison to networks applied to other", "bbox": {"l": 308.86197, "t": 381.14529000000005, "r": 505.46395999999993, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "problems", "bbox": {"l": 508.03430000000003, "t": 381.14529000000005, "r": 545.11511, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "(e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d),", "bbox": {"l": 308.86197, "t": 393.10028, "r": 517.68799, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "some-", "bbox": {"l": 520.76642, "t": 393.10028, "r": 545.11499, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "thing which we relate to the simplicity of table images.", "bbox": {"l": 308.86197, "t": 405.05526999999995, "r": 528.01935, "b": 413.96182, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 417.11426, "r": 545.11511, "b": 545.57271, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"id": 169, "text": "The transformer encoder receives an encoded", "bbox": {"l": 320.81696, "t": 417.11426, "r": 515.49609, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "image", "bbox": {"l": 520.7663, "t": 417.11426, "r": 545.11487, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "from the", "bbox": {"l": 308.86197, "t": 429.0692399999999, "r": 343.72107, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "CNN Backbone Network", "bbox": {"l": 347.03796, "t": 429.15891, "r": 446.45471000000003, "b": 437.74667, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "and refines it", "bbox": {"l": 449.93996999999996, "t": 429.0692399999999, "r": 503.06055000000003, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "through", "bbox": {"l": 506.37808, "t": 429.0692399999999, "r": 537.3717, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a", "bbox": {"l": 540.68927, "t": 429.0692399999999, "r": 545.11267, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "multi-head dot-product attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "caption", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 50.112, "t": 527.7828099999999, "r": 286.36597, "b": 680.27094, "coord_origin": "TOPLEFT"}, "confidence": 0.8913399577140808, "cells": [{"id": 107, "text": "Figure 4: Given an input image of a table, the", "bbox": {"l": 50.112, "t": 527.90237, "r": 229.78752, "b": 536.80893, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Encoder", "bbox": {"l": 231.787, "t": 527.7828099999999, "r": 267.76196, "b": 536.7392, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "pro-", "bbox": {"l": 269.76401, "t": 527.90237, "r": 286.36169, "b": 536.80893, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "duces fixed-length features that represent the input image.", "bbox": {"l": 50.112015, "t": 539.85738, "r": 286.36508, "b": 548.76393, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "Structure", "bbox": {"l": 245.59502, "t": 563.64882, "r": 286.362, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Decoder", "bbox": {"l": 50.112015, "t": 575.60382, "r": 85.519089, "b": 584.5602, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "receives \u2018tokenized tags\u2019 of the HTML code that", "bbox": {"l": 88.623016, "t": 575.7233699999999, "r": 286.36072, "b": 584.6299300000001, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "represent the table structure. Afterwards, a transformer en-", "bbox": {"l": 50.112015, "t": 587.6783800000001, "r": 286.36511, "b": 596.58493, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder and decoder architecture is employed to produce fea-", "bbox": {"l": 50.112015, "t": 599.63338, "r": 286.36508, "b": 608.53993, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "tures that are received by a linear layer, and the", "bbox": {"l": 50.112015, "t": 611.58838, "r": 240.43756000000002, "b": 620.4949300000001, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Cell BBox", "bbox": {"l": 243.19801, "t": 611.46883, "r": 286.36597, "b": 620.4252, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Decoder. The linear layer is applied to the features to", "bbox": {"l": 50.112015, "t": 623.42482, "r": 286.36511, "b": 632.3812, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "predict the tags. Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives \u2018tokenized tags\u2019 of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (\u2018 < td > \u2019, \u2018 < \u2019) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > \u2019 and \u2018 < \u2019 HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}], "headers": [{"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}, "confidence": 0.8719567656517029, "cells": [{"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423347115516663, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, 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"text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880596995353699, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441831469535828, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Transformer Encoder consists of two \u201cTransformer Encoder Layers\u201d, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \u201cTransformer Decoder Layers\u201d with similar input and output dimensions as the \u201cTransformer Encoder Layers\u201d. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a \u2019caching\u2019 technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423347115516663, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 \u2212 \u03bb ) l$_{box}$ (1)"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880596995353699, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441831469535828, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}], "body": [{"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Transformer Encoder consists of two \u201cTransformer Encoder Layers\u201d, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \u201cTransformer Decoder Layers\u201d with similar input and output dimensions as the \u201cTransformer Encoder Layers\u201d. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a \u2019caching\u2019 technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423347115516663, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 \u2212 \u03bb ) l$_{box}$ (1)"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880596995353699, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441831469535828, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}], "headers": [{"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 247.74349999999998, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "All", "bbox": {"l": 264.54044, "t": 426.66736, "r": 277.27264, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "EDD", "bbox": {"l": 81.612, "t": 443.62436, "r": 102.08514, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "PTN", "bbox": {"l": 134.87206, "t": 443.62436, "r": 153.69141, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "91.1", "bbox": {"l": 176.56554, "t": 443.62436, "r": 194.00009, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "88.7", "bbox": {"l": 220.82938000000001, "t": 443.62436, "r": 238.26393, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "89.9", "bbox": {"l": 262.18414, "t": 443.62436, "r": 279.61868, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "GTE", "bbox": {"l": 82.165001, "t": 455.58035, "r": 101.5323, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PTN", "bbox": {"l": 134.86716, "t": 455.58035, "r": 153.68651, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "-", "bbox": {"l": 183.62411, "t": 455.58035, "r": 186.94167, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "-", "bbox": {"l": 227.88795000000002, "t": 455.58035, "r": 231.20551, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "93.01", "bbox": {"l": 259.69855, "t": 455.58035, "r": 282.11441, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 468.13336, "r": 117.38329000000002, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PTN", "bbox": {"l": 134.86766, "t": 468.13336, "r": 153.68701, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "98.5", "bbox": {"l": 176.57111, "t": 468.13336, "r": 194.00566, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "95.0", "bbox": {"l": 220.83495, "t": 468.13336, "r": 238.26950000000002, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "96.75", "bbox": {"l": 259.698, "t": 468.01379, "r": 282.11386, "b": 476.97018, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "EDD", "bbox": {"l": 81.612, "t": 483.32635, "r": 102.08514, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "FTN", "bbox": {"l": 134.87206, "t": 483.32635, "r": 153.69141, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "88.4", "bbox": {"l": 176.56554, "t": 483.32635, "r": 194.00009, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "92.08", "bbox": {"l": 218.33870999999996, "t": 483.32635, "r": 240.75455999999997, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "90.6", "bbox": {"l": 262.18411, "t": 483.32635, "r": 279.61865, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "GTE", "bbox": {"l": 82.165001, "t": 495.28134, "r": 101.5323, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "FTN", "bbox": {"l": 134.86716, "t": 495.28134, "r": 153.68651, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "-", "bbox": {"l": 183.62411, "t": 495.28134, "r": 186.94167, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "-", "bbox": {"l": 227.88795000000002, "t": 495.28134, "r": 231.20551, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "87.14", "bbox": {"l": 259.69855, "t": 495.28134, "r": 282.11441, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "GTE (FT)", "bbox": {"l": 71.789001, "t": 507.23633, "r": 111.90838999999998, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "FTN", "bbox": {"l": 134.86221, "t": 507.23633, "r": 153.68156, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "-", "bbox": {"l": 183.62914, "t": 507.23633, "r": 186.94669, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "-", "bbox": {"l": 227.89297, "t": 507.23633, "r": 231.21053000000003, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "91.02", "bbox": {"l": 259.6936, "t": 507.23633, "r": 282.10947, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 519.1913099999999, "r": 117.38329000000002, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "FTN", "bbox": {"l": 134.86766, "t": 519.1913099999999, "r": 153.68701, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "97.5", "bbox": {"l": 176.57111, "t": 519.1913099999999, "r": 194.00566, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "96.0", "bbox": {"l": 220.83495, "t": 519.1913099999999, "r": 238.26950000000002, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "96.8", "bbox": {"l": 262.189, "t": 519.0717500000001, "r": 279.62354, "b": 528.02814, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "EDD", "bbox": {"l": 81.612, "t": 536.49837, "r": 102.08514, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "TB", "bbox": {"l": 137.91064, "t": 536.49837, "r": 150.64285, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "86.0", "bbox": {"l": 176.56554, "t": 536.49837, "r": 194.00009, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "-", "bbox": {"l": 227.89285, "t": 536.49837, "r": 231.21040000000002, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "86.0", "bbox": {"l": 262.18411, "t": 536.49837, "r": 279.61865, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 548.45436, "r": 117.38329000000002, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "TB", "bbox": {"l": 137.90625, "t": 548.45436, "r": 150.63846, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "89.6", "bbox": {"l": 176.57111, "t": 548.45436, "r": 194.00566, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "-", "bbox": {"l": 227.88845999999998, "t": 548.45436, "r": 231.20601, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "89.6", "bbox": {"l": 262.189, "t": 548.3348100000001, "r": 279.62354, "b": 557.2911799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 568.00237, "r": 117.38329000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "STN", "bbox": {"l": 134.86766, "t": 568.00237, "r": 153.68701, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "96.9", "bbox": {"l": 176.57111, "t": 568.00237, "r": 194.00566, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "95.7", "bbox": {"l": 220.83495, "t": 568.00237, "r": 238.26950000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "96.7", "bbox": {"l": 262.1897, "t": 568.00237, "r": 279.62424, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 270.62134000000003, "r": 377.00076, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "PubTabNet", "bbox": {"l": 393.69809, "t": 270.62134000000003, "r": 438.28073, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "79.2", "bbox": {"l": 455.63559, "t": 270.62134000000003, "r": 473.07013, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "82.7", "bbox": {"l": 498.16592, "t": 270.62134000000003, "r": 515.60046, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 282.57631999999995, "r": 377.86331, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "PubTabNet", "bbox": {"l": 393.69388, "t": 282.57631999999995, "r": 438.27652, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "82.1", "bbox": {"l": 455.63101, "t": 282.45676, "r": 473.06555000000003, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "86.8", "bbox": {"l": 498.1713, "t": 282.45676, "r": 515.60583, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 294.53131, "r": 377.86331, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "SynthTabNet", "bbox": {"l": 389.81842, "t": 294.53131, "r": 442.15194999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "87.7", "bbox": {"l": 455.63135, "t": 294.53131, "r": 473.06589, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "-", "bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "Table 3:", "bbox": {"l": 308.862, "t": 316.44931, "r": 341.49951, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Cell Bounding Box detection results on PubTab-", "bbox": {"l": 348.60284, "t": 316.44931, "r": 545.11517, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "Net, and FinTabNet. PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Model", "bbox": {"l": 358.01099, "t": 552.23337, "r": 384.02335, "b": 561.1399200000001, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "TEDS", "bbox": {"l": 449.03400000000005, "t": 546.25537, "r": 473.94049000000007, "b": 555.16193, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "Simple", "bbox": {"l": 408.50598, "t": 558.21037, "r": 436.73999, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Complex", "bbox": {"l": 448.6951, "t": 558.21037, "r": 485.07849, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "All", "bbox": {"l": 499.3848, "t": 558.21037, "r": 512.117, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "Tabula", "bbox": {"l": 357.68201, "t": 575.16736, "r": 384.3519, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "78.0", "bbox": {"l": 413.90097, "t": 575.16736, "r": 431.33550999999994, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "57.8", "bbox": {"l": 458.16479000000004, "t": 575.16736, "r": 475.59933000000007, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "67.9", "bbox": {"l": 497.0289, "t": 575.16736, "r": 514.46344, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "Traprange", "bbox": {"l": 350.72299, "t": 587.12236, "r": 391.31064, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "60.8", "bbox": {"l": 413.90582, "t": 587.12236, "r": 431.34036, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "49.9", "bbox": {"l": 458.16965, "t": 587.12236, "r": 475.60419, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "55.4", "bbox": {"l": 497.03374999999994, "t": 587.12236, "r": 514.46832, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Camelot", "bbox": {"l": 354.13599, "t": 599.07835, "r": 387.89923, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "80.0", "bbox": {"l": 413.90161, "t": 599.07835, "r": 431.33615, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "66.0", "bbox": {"l": 458.16544, "t": 599.07835, "r": 475.59998, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "73.0", "bbox": {"l": 497.02954000000005, "t": 599.07835, "r": 514.46411, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Acrobat Pro", "bbox": {"l": 346.55899, "t": 611.03336, "r": 395.47534, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "68.9", "bbox": {"l": 413.90616, "t": 611.03336, "r": 431.34069999999997, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "61.8", "bbox": {"l": 458.16998000000007, "t": 611.03336, "r": 475.60452, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "65.3", "bbox": {"l": 497.03409, "t": 611.03336, "r": 514.46863, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "EDD", "bbox": {"l": 360.78101, "t": 622.9883600000001, "r": 381.25415, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "91.2", "bbox": {"l": 413.90158, "t": 622.9883600000001, "r": 431.33612, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "85.4", "bbox": {"l": 458.16541, "t": 622.9883600000001, "r": 475.59995000000004, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "88.3", "bbox": {"l": 497.0295100000001, "t": 622.9883600000001, "r": 514.46405, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "TableFormer", "bbox": {"l": 345.483, "t": 634.94336, "r": 396.5513, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "95.4", "bbox": {"l": 413.90616, "t": 634.94336, "r": 431.34069999999997, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "90.1", "bbox": {"l": 458.16998000000007, "t": 634.94336, "r": 475.60452, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "93.6", "bbox": {"l": 497.03400000000005, "t": 634.82381, "r": 514.46857, "b": 643.78018, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "section_header", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}, "confidence": 0.9554283022880554, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500426650047302, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735332727432251, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855114221572876, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "table", "bbox": {"l": 308.40673828125, "t": 247.87644958496094, "r": 533.6420288085938, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691707491874695, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 270.62134000000003, "r": 377.00076, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "PubTabNet", "bbox": {"l": 393.69809, "t": 270.62134000000003, "r": 438.28073, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "79.2", "bbox": {"l": 455.63559, "t": 270.62134000000003, "r": 473.07013, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "82.7", "bbox": {"l": 498.16592, "t": 270.62134000000003, "r": 515.60046, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 282.57631999999995, "r": 377.86331, "b": 291.48288, 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. 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Datasets and Metrics"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "our Cell BBox Decoder accuracy for cells with a class label of \u2018content\u2019 only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we\u2019ve integrated TableFormer\u2019s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"label": "formula", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500426650047302, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 \u2212 EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735332727432251, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855114221572876, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "table", "bbox": {"l": 308.40673828125, "t": 247.87644958496094, "r": 533.6420288085938, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691707491874695, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 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303.43787, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "87.7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "-", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 308.862, "t": 316.44931, "r": 545.11517, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9519907236099243, "cells": [{"id": 162, "text": "Table 3:", "bbox": {"l": 308.862, "t": 316.44931, "r": 341.49951, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Cell Bounding Box detection results on PubTab-", "bbox": {"l": 348.60284, "t": 316.44931, "r": 545.11517, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "Net, and FinTabNet. PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 53.36848831176758, "t": 409.1356506347656, "r": 283.04437255859375, "b": 582.397705078125, "coord_origin": "TOPLEFT"}, "confidence": 0.989250659942627, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, 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"coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "93.6", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 15, "page_no": 6, "cluster": {"id": 15, "label": "text", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 613.29492, "coord_origin": "TOPLEFT"}, "confidence": 0.7209141850471497, "cells": [{"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"label": "text", "id": 16, "page_no": 6, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433366537094116, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "FT: Model was trained on PubTabNet then finetuned."}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854632616043091, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "caption", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 308.862, "t": 656.86136, "r": 545.11517, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9541405439376831, "cells": [{"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}, {"label": "page_footer", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}, "confidence": 0.8787976503372192, "cells": [{"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}], "body": [{"label": "section_header", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}, "confidence": 0.9554283022880554, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.3. Datasets and Metrics"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "our Cell BBox Decoder accuracy for cells with a class label of \u2018content\u2019 only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we\u2019ve integrated TableFormer\u2019s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"label": "formula", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500426650047302, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 \u2212 EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735332727432251, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855114221572876, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "table", "bbox": {"l": 308.40673828125, "t": 247.87644958496094, "r": 533.6420288085938, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691707491874695, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 270.62134000000003, "r": 377.00076, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "PubTabNet", "bbox": {"l": 393.69809, "t": 270.62134000000003, "r": 438.28073, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "79.2", "bbox": {"l": 455.63559, "t": 270.62134000000003, "r": 473.07013, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "82.7", "bbox": {"l": 498.16592, "t": 270.62134000000003, "r": 515.60046, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 282.57631999999995, "r": 377.86331, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "PubTabNet", "bbox": {"l": 393.69388, "t": 282.57631999999995, "r": 438.27652, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "82.1", "bbox": {"l": 455.63101, "t": 282.45676, "r": 473.06555000000003, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "86.8", "bbox": {"l": 498.1713, "t": 282.45676, "r": 515.60583, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 294.53131, "r": 377.86331, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "SynthTabNet", "bbox": {"l": 389.81842, "t": 294.53131, "r": 442.15194999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "87.7", "bbox": {"l": 455.63135, "t": 294.53131, "r": 473.06589, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "-", "bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}], "children": [{"id": 73, "label": "text", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}], 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Box detection results on PubTab-", "bbox": {"l": 348.60284, "t": 316.44931, "r": 545.11517, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "Net, and FinTabNet. PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 53.36848831176758, "t": 409.1356506347656, "r": 283.04437255859375, "b": 582.397705078125, "coord_origin": "TOPLEFT"}, "confidence": 0.989250659942627, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, 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"cluster": {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433366537094116, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "FT: Model was trained on PubTabNet then finetuned."}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854632616043091, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "caption", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 308.862, "t": 656.86136, "r": 545.11517, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9541405439376831, "cells": [{"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}], "headers": [{"label": "page_footer", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}, "confidence": 0.8787976503372192, "cells": [{"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "b.", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 62.219952, "b": 216.10645, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Structure predicted by TableFormer, with superimposed matched PDF cell text:", "bbox": {"l": 66.424026, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Japanese language (previously unseen by TableFormer):", "bbox": {"l": 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"TOPLEFT"}}, {"id": 180, "text": "Predicted Structure", "bbox": {"l": 384.35437, "t": 381.77722, "r": 430.99261, "b": 386.44281, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "bbox": {"l": 328.78101, "t": 704.920792, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "8", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 20, "label": "list_item", "bbox": {"l": 53.286037, "t": 78.68756000000008, "r": 61.550289, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.5819774866104126, "cells": [{"id": 4, "text": "a.", "bbox": {"l": 53.286037, "t": 78.68756000000008, "r": 61.550289, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "list_item", "bbox": {"l": 65.682419, "t": 78.68756000000008, "r": 499.55563, "b": 86.56073000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.5412202477455139, "cells": [{"id": 5, "text": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "bbox": {"l": 65.682419, "t": 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This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852355718612671, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. 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"section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436547756195068, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}, "confidence": 0.9561256170272827, "cells": [{"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.5. Qualitative Analysis"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875592589378357, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \u201cSynthTabNet\u201d a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852355718612671, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various \u201ccomplex\u201d tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}, "confidence": 0.9442476034164429, "cells": [{"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}, "confidence": 0.8318724036216736, "cells": [{"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "bbox": {"l": 328.78101, "t": 704.920792, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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"section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436547756195068, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}, "confidence": 0.9561256170272827, "cells": [{"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.5. Qualitative Analysis"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875592589378357, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \u201cSynthTabNet\u201d a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852355718612671, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various \u201ccomplex\u201d tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}, "confidence": 0.9442476034164429, "cells": [{"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}, "confidence": 0.8318724036216736, "cells": [{"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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Winter Conference for Applications in Computer Vision (WACV) , 2021. 2, 3"}, {"label": "list_item", "id": 8, "page_no": 9, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112015, "t": 693.961502, "r": 286.36334, "b": 712.936462, "coord_origin": "TOPLEFT"}, "confidence": 0.838570237159729, "cells": [{"id": 106, "text": "[37]", "bbox": {"l": 50.112015, "t": 693.961502, "r": 66.506706, "b": 701.977463, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Xu", "bbox": {"l": 68.966896, "t": 693.961502, "r": 80.992294, "b": 701.977463, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Zhong,", "bbox": {"l": 89.062057, "t": 693.961502, "r": 114.71492999999998, "b": 701.977463, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Elaheh", "bbox": {"l": 124.24621000000002, "t": 693.961502, "r": 149.1459, "b": 701.977463, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "ShafieiBavani,", "bbox": {"l": 157.22462, "t": 693.961502, "r": 209.37321, "b": 701.977463, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "and", "bbox": {"l": 218.9045, "t": 693.961502, "r": 231.85196999999997, "b": 701.977463, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Antonio", "bbox": {"l": 239.93069, "t": 693.961502, "r": 269.32254, "b": 701.977463, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Ji-", "bbox": {"l": 277.3923, "t": 693.961502, "r": 286.3587, "b": 701.977463, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "meno Yepes. Image-based table recognition: Data, model,", "bbox": {"l": 70.031013, "t": 704.920502, "r": 286.36334, "b": 712.936462, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[37] Xu Zhong, Elaheh ShafieiBavani, and Antonio Jimeno Yepes. Image-based table recognition: Data, model,"}], "headers": [{"label": "page_footer", "id": 0, "page_no": 9, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 292.63, "t": 734.1329920000001, "r": 302.59259, "b": 743.039555, "coord_origin": "TOPLEFT"}, "confidence": 0.9069585204124451, "cells": [{"id": 127, "text": "10", "bbox": {"l": 292.63, "t": 734.1329920000001, "r": 302.59259, "b": 743.039555, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}]}}, {"page_no": 10, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497079730033875, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596062302589417, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699996113777161, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568929672241211, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335852265357971, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.978333592414856, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986662030220032, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497079730033875, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Details on the datasets"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596062302589417, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699996113777161, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568929672241211, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335852265357971, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.978333592414856, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986662030220032, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}], "body": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497079730033875, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Details on the datasets"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596062302589417, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699996113777161, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568929672241211, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335852265357971, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.978333592414856, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}], "headers": [{"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986662030220032, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "FinTabNet", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}}, {"id": 3, 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"text": "47K", "bbox": {"l": 345.69101, "t": 86.05591000000004, "r": 355.70944, "b": 91.01312000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Simple", "bbox": {"l": 508.54248, "t": 141.37683000000004, "r": 526.00592, "b": 146.33405000000005, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "145K", "bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 30, "label": "picture", "bbox": {"l": 53.54228973388672, "t": 74.74851989746094, "r": 544.938232421875, "b": 147.5908966064453, "coord_origin": "TOPLEFT"}, "confidence": 0.6033812761306763, "cells": [], "children": [{"id": 27, "label": "text", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 3, "text": "Table Bank", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "FinTabNet", "bbox": {"l": 289.5791, "t": 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Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.8942293524742126, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.96295565366745, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423391819000244, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767084717750549, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562006592750549, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347665309906006, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280701637268066, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126085042953491, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491711854934692, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587164521217346, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459475636482239, "cells": [{"id": 168, "text": "9a. 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The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.96295565366745, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423391819000244, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767084717750549, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562006592750549, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347665309906006, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280701637268066, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126085042953491, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as \u201cgood\u201d ones and \u201cbad\u201d ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491711854934692, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587164521217346, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459475636482239, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716898918151855, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9b. Intersect the orphan\u2019s bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298409819602966, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } \u2212 min { x$_{c}$ } (4)"}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584902882575989, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "text", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.759074866771698, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9d. Intersect the orphan\u2019s bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.290222, "r": 286.36496, "b": 713.151787, "coord_origin": "TOPLEFT"}, "confidence": 0.9260510802268982, "cells": [{"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}, {"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126599431037903, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}], "body": [{"label": "picture", "id": 30, "page_no": 11, "cluster": {"id": 30, "label": "picture", "bbox": {"l": 53.54228973388672, "t": 74.74851989746094, "r": 544.938232421875, "b": 147.5908966064453, "coord_origin": "TOPLEFT"}, "confidence": 0.6033812761306763, "cells": [], "children": [{"id": 27, "label": "text", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 1, "text": 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Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"label": "text", "id": 16, "page_no": 11, "cluster": {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.8942293524742126, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.96295565366745, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423391819000244, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767084717750549, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562006592750549, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347665309906006, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280701637268066, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126085042953491, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as \u201cgood\u201d ones and \u201cbad\u201d ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491711854934692, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587164521217346, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459475636482239, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716898918151855, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9b. Intersect the orphan\u2019s bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298409819602966, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } \u2212 min { x$_{c}$ } (4)"}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584902882575989, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "text", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.759074866771698, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9d. Intersect the orphan\u2019s bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.290222, "r": 286.36496, "b": 713.151787, "coord_origin": "TOPLEFT"}, "confidence": 0.9260510802268982, "cells": [{"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}], "headers": [{"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126599431037903, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "phan cell.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 88.846588, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9f. Otherwise create a new structural cell and match it", "bbox": {"l": 62.067001, "t": 87.16339000000005, "r": 286.36496, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "wit the orphan cell.", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 127.03322, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Aditional images with examples of TableFormer predic-", "bbox": {"l": 62.067001, "t": 111.16309000000001, "r": 286.36499, "b": 119.7508499999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tions and post-processing can be found below.", "bbox": {"l": 50.112, "t": 123.11810000000003, "r": 234.06139999999996, "b": 131.70587, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Figure 8: Example of a table with multi-line header.", "bbox": {"l": 63.341, "t": 502.05637, "r": 273.13342, "b": 510.96292, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Figure 9:", "bbox": {"l": 308.862, "t": 306.59836, "r": 345.63397, "b": 315.50491, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Example of a table with big empty distance be-", "bbox": {"l": 352.78711, "t": 306.59836, "r": 545.11511, "b": 315.50491, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "tween cells.", "bbox": {"l": 308.862, "t": 318.55334, "r": 355.89545, "b": 327.45990000000006, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Figure 10: Example of a complex table with empty cells.", "bbox": {"l": 312.34299, "t": 680.4933599999999, "r": 541.63232, "b": 689.39993, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "13", "bbox": {"l": 292.63098, "t": 734.133358, "r": 302.59357, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 88.846588, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.7545604109764099, "cells": [{"id": 0, "text": "phan cell.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 88.846588, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36496, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9170765280723572, "cells": [{"id": 1, "text": "9f. Otherwise create a new structural cell and match it", "bbox": {"l": 62.067001, "t": 87.16339000000005, "r": 286.36496, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "wit the orphan cell.", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 127.03322, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "table", "bbox": {"l": 310.3294372558594, "t": 101.17761993408203, "r": 555.8338623046875, "b": 136.14747619628906, "coord_origin": "TOPLEFT"}, "confidence": 0.7048211097717285, "cells": [], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 111.16309000000001, "r": 286.36499, "b": 131.70587, "coord_origin": "TOPLEFT"}, "confidence": 0.9454684257507324, "cells": [{"id": 3, "text": "Aditional images with examples of TableFormer predic-", "bbox": {"l": 62.067001, "t": 111.16309000000001, "r": 286.36499, "b": 119.7508499999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tions and post-processing can be 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\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/2206.01062.doctags.txt b/tests/data/groundtruth/docling_v2/2206.01062.doctags.txt
index 215e2082..6269eb06 100644
--- a/tests/data/groundtruth/docling_v2/2206.01062.doctags.txt
+++ b/tests/data/groundtruth/docling_v2/2206.01062.doctags.txt
@@ -58,9 +58,9 @@
 <section_header_level_1><location><page_3><loc_52><loc_22><loc_77><loc_23></location>4 ANNOTATION CAMPAIGN</section_header_level_1>
 <text><location><page_3><loc_52><loc_11><loc_91><loc_20></location>The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,</text>
 <table>
-<location><page_4><loc_17><loc_63><loc_83><loc_82></location>
+<location><page_4><loc_16><loc_63><loc_84><loc_83></location>
 <caption>Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.</caption>
-<row_0><col_0><body></col_0><col_1><body></col_1><col_2><col_header>% of Total</col_2><col_3><col_header>% of Total</col_3><col_4><col_header>% of Total</col_4><col_5><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_5><col_6><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_6><col_7><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_7><col_8><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_8><col_9><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_9><col_10><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_10><col_11><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_11></row_0>
+<row_0><col_0><body></col_0><col_1><body></col_1><col_2><col_header>% of Total</col_2><col_3><col_header>% of Total</col_3><col_4><col_header>% of Total</col_4><col_5><col_header>% of Total</col_5><col_6><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_6><col_7><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_7><col_8><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_8><col_9><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_9><col_10><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_10><col_11><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_11></row_0>
 <row_1><col_0><col_header>class label</col_0><col_1><col_header>Count</col_1><col_2><col_header>Train</col_2><col_3><col_header>Test</col_3><col_4><col_header>Val</col_4><col_5><col_header>All</col_5><col_6><col_header>Fin</col_6><col_7><col_header>Man</col_7><col_8><col_header>Sci</col_8><col_9><col_header>Law</col_9><col_10><col_header>Pat</col_10><col_11><col_header>Ten</col_11></row_1>
 <row_2><col_0><row_header>Caption</col_0><col_1><body>22524</col_1><col_2><body>2.04</col_2><col_3><body>1.77</col_3><col_4><body>2.32</col_4><col_5><body>84-89</col_5><col_6><body>40-61</col_6><col_7><body>86-92</col_7><col_8><body>94-99</col_8><col_9><body>95-99</col_9><col_10><body>69-78</col_10><col_11><body>n/a</col_11></row_2>
 <row_3><col_0><row_header>Footnote</col_0><col_1><body>6318</col_1><col_2><body>0.60</col_2><col_3><body>0.31</col_3><col_4><body>0.58</col_4><col_5><body>83-91</col_5><col_6><body>n/a</col_6><col_7><body>100</col_7><col_8><body>62-88</col_8><col_9><body>85-94</col_9><col_10><body>n/a</col_10><col_11><body>82-97</col_11></row_3>
@@ -105,7 +105,7 @@
 <text><location><page_5><loc_52><loc_31><loc_91><loc_34></location>were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.</text>
 <text><location><page_5><loc_52><loc_10><loc_91><loc_31></location>Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted</text>
 <table>
-<location><page_6><loc_11><loc_56><loc_46><loc_75></location>
+<location><page_6><loc_10><loc_56><loc_47><loc_75></location>
 <caption>Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.</caption>
 <row_0><col_0><body></col_0><col_1><col_header>human</col_1><col_2><col_header>MRCNN</col_2><col_3><col_header>MRCNN</col_3><col_4><col_header>FRCNN</col_4><col_5><col_header>YOLO</col_5></row_0>
 <row_1><col_0><body></col_0><col_1><col_header>human</col_1><col_2><col_header>R50</col_2><col_3><col_header>R101</col_3><col_4><col_header>R101</col_4><col_5><col_header>v5x6</col_5></row_1>
@@ -135,7 +135,7 @@
 <text><location><page_6><loc_52><loc_11><loc_91><loc_35></location>In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.</text>
 <text><location><page_7><loc_9><loc_84><loc_48><loc_89></location>Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.</text>
 <table>
-<location><page_7><loc_14><loc_63><loc_43><loc_81></location>
+<location><page_7><loc_13><loc_63><loc_44><loc_81></location>
 <caption>Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.</caption>
 <row_0><col_0><col_header>Class-count</col_0><col_1><col_header>11</col_1><col_2><col_header>6</col_2><col_3><col_header>5</col_3><col_4><col_header>4</col_4></row_0>
 <row_1><col_0><row_header>Caption</col_0><col_1><body>68</col_1><col_2><body>Text</col_2><col_3><body>Text</col_3><col_4><body>Text</col_4></row_1>
@@ -151,8 +151,12 @@
 <row_11><col_0><row_header>Title</col_0><col_1><body>77</col_1><col_2><body>Sec.-h.</col_2><col_3><body>Sec.-h.</col_3><col_4><body>Sec.-h.</col_4></row_11>
 <row_12><col_0><row_header>Overall</col_0><col_1><body>72</col_1><col_2><body>73</col_2><col_3><body>78</col_3><col_4><body>77</col_4></row_12>
 </table>
+<section_header_level_1><location><page_7><loc_9><loc_58><loc_21><loc_60></location>Learning Curve</section_header_level_1>
+<text><location><page_7><loc_9><loc_33><loc_48><loc_58></location>One of the fundamental questions related to any dataset is if it is "large enough". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.</text>
+<section_header_level_1><location><page_7><loc_9><loc_30><loc_27><loc_32></location>Impact of Class Labels</section_header_level_1>
+<text><location><page_7><loc_9><loc_11><loc_48><loc_30></location>The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption → Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of</text>
 <table>
-<location><page_7><loc_59><loc_62><loc_85><loc_81></location>
+<location><page_7><loc_58><loc_61><loc_85><loc_81></location>
 <row_0><col_0><body>Class-count</col_0><col_1><col_header>11</col_1><col_2><col_header>11</col_2><col_3><col_header>5</col_3><col_4><col_header>5</col_4></row_0>
 <row_1><col_0><body>Split</col_0><col_1><col_header>Doc</col_1><col_2><col_header>Page</col_2><col_3><col_header>Doc</col_3><col_4><col_header>Page</col_4></row_1>
 <row_2><col_0><row_header>Caption</col_0><col_1><body>68</col_1><col_2><body>83</col_2><col_3><body></col_3><col_4><body></col_4></row_2>
@@ -168,33 +172,29 @@
 <row_12><col_0><row_header>Title</col_0><col_1><body>77</col_1><col_2><body>81</col_2><col_3><body></col_3><col_4><body></col_4></row_12>
 <row_13><col_0><row_header>All</col_0><col_1><body>72</col_1><col_2><body>84</col_2><col_3><body>78</col_3><col_4><body>87</col_4></row_13>
 </table>
-<section_header_level_1><location><page_7><loc_9><loc_58><loc_21><loc_60></location>Learning Curve</section_header_level_1>
-<text><location><page_7><loc_9><loc_33><loc_48><loc_58></location>One of the fundamental questions related to any dataset is if it is "large enough". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.</text>
-<section_header_level_1><location><page_7><loc_9><loc_30><loc_27><loc_32></location>Impact of Class Labels</section_header_level_1>
-<text><location><page_7><loc_9><loc_11><loc_48><loc_30></location>The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption → Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of</text>
 <text><location><page_7><loc_52><loc_47><loc_91><loc_58></location>lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.</text>
 <section_header_level_1><location><page_7><loc_52><loc_44><loc_90><loc_46></location>Impact of Document Split in Train and Test Set</section_header_level_1>
 <text><location><page_7><loc_52><loc_25><loc_91><loc_44></location>Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains ˜ 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided.</text>
 <section_header_level_1><location><page_7><loc_52><loc_22><loc_68><loc_23></location>Dataset Comparison</section_header_level_1>
 <text><location><page_7><loc_52><loc_11><loc_91><loc_21></location>Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,</text>
 <table>
-<location><page_8><loc_13><loc_57><loc_44><loc_78></location>
+<location><page_8><loc_12><loc_57><loc_45><loc_78></location>
 <caption>Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.</caption>
 <row_0><col_0><body></col_0><col_1><body></col_1><col_2><col_header>Testing on</col_2><col_3><col_header>Testing on</col_3><col_4><col_header>Testing on</col_4></row_0>
 <row_1><col_0><col_header>Training on</col_0><col_1><col_header>labels</col_1><col_2><col_header>PLN</col_2><col_3><col_header>DB</col_3><col_4><col_header>DLN</col_4></row_1>
-<row_2><col_0><body></col_0><col_1><body>Figure</col_1><col_2><body>96</col_2><col_3><body>43</col_3><col_4><body>23</col_4></row_2>
-<row_3><col_0><body></col_0><col_1><body>Sec-header</col_1><col_2><body>87</col_2><col_3><body>-</col_3><col_4><body>32</col_4></row_3>
-<row_4><col_0><body>PubLayNet (PLN)</col_0><col_1><body>Table</col_1><col_2><body>95</col_2><col_3><body>24</col_3><col_4><body>49</col_4></row_4>
-<row_5><col_0><body></col_0><col_1><body>Text</col_1><col_2><body>96</col_2><col_3><body>-</col_3><col_4><body>42</col_4></row_5>
-<row_6><col_0><body></col_0><col_1><body>total</col_1><col_2><body>93</col_2><col_3><body>34</col_3><col_4><body>30</col_4></row_6>
-<row_7><col_0><body></col_0><col_1><body>Figure</col_1><col_2><body>77</col_2><col_3><body>71</col_3><col_4><body>31</col_4></row_7>
-<row_8><col_0><body>DocBank (DB)</col_0><col_1><body>Table</col_1><col_2><body>19</col_2><col_3><body>65</col_3><col_4><body>22</col_4></row_8>
-<row_9><col_0><body></col_0><col_1><body>total</col_1><col_2><body>48</col_2><col_3><body>68</col_3><col_4><body>27</col_4></row_9>
-<row_10><col_0><body></col_0><col_1><body>Figure</col_1><col_2><body>67</col_2><col_3><body>51</col_3><col_4><body>72</col_4></row_10>
-<row_11><col_0><body></col_0><col_1><body>Sec-header</col_1><col_2><body>53</col_2><col_3><body>-</col_3><col_4><body>68</col_4></row_11>
-<row_12><col_0><body>DocLayNet (DLN)</col_0><col_1><body>Table</col_1><col_2><body>87</col_2><col_3><body>43</col_3><col_4><body>82</col_4></row_12>
-<row_13><col_0><body></col_0><col_1><body>Text</col_1><col_2><body>77</col_2><col_3><body>-</col_3><col_4><body>84</col_4></row_13>
-<row_14><col_0><body></col_0><col_1><body>total</col_1><col_2><body>59</col_2><col_3><body>47</col_3><col_4><body>78</col_4></row_14>
+<row_2><col_0><row_header>PubLayNet (PLN)</col_0><col_1><row_header>Figure</col_1><col_2><body>96</col_2><col_3><body>43</col_3><col_4><body>23</col_4></row_2>
+<row_3><col_0><row_header>PubLayNet (PLN)</col_0><col_1><row_header>Sec-header</col_1><col_2><body>87</col_2><col_3><body>-</col_3><col_4><body>32</col_4></row_3>
+<row_4><col_0><row_header>PubLayNet (PLN)</col_0><col_1><row_header>Table</col_1><col_2><body>95</col_2><col_3><body>24</col_3><col_4><body>49</col_4></row_4>
+<row_5><col_0><row_header>PubLayNet (PLN)</col_0><col_1><row_header>Text</col_1><col_2><body>96</col_2><col_3><body>-</col_3><col_4><body>42</col_4></row_5>
+<row_6><col_0><row_header>PubLayNet (PLN)</col_0><col_1><row_header>total</col_1><col_2><body>93</col_2><col_3><body>34</col_3><col_4><body>30</col_4></row_6>
+<row_7><col_0><row_header>DocBank (DB)</col_0><col_1><row_header>Figure</col_1><col_2><body>77</col_2><col_3><body>71</col_3><col_4><body>31</col_4></row_7>
+<row_8><col_0><row_header>DocBank (DB)</col_0><col_1><row_header>Table</col_1><col_2><body>19</col_2><col_3><body>65</col_3><col_4><body>22</col_4></row_8>
+<row_9><col_0><row_header>DocBank (DB)</col_0><col_1><row_header>total</col_1><col_2><body>48</col_2><col_3><body>68</col_3><col_4><body>27</col_4></row_9>
+<row_10><col_0><row_header>DocLayNet (DLN)</col_0><col_1><row_header>Figure</col_1><col_2><body>67</col_2><col_3><body>51</col_3><col_4><body>72</col_4></row_10>
+<row_11><col_0><row_header>DocLayNet (DLN)</col_0><col_1><row_header>Sec-header</col_1><col_2><body>53</col_2><col_3><body>-</col_3><col_4><body>68</col_4></row_11>
+<row_12><col_0><row_header>DocLayNet (DLN)</col_0><col_1><row_header>Table</col_1><col_2><body>87</col_2><col_3><body>43</col_3><col_4><body>82</col_4></row_12>
+<row_13><col_0><row_header>DocLayNet (DLN)</col_0><col_1><row_header>Text</col_1><col_2><body>77</col_2><col_3><body>-</col_3><col_4><body>84</col_4></row_13>
+<row_14><col_0><row_header>DocLayNet (DLN)</col_0><col_1><row_header>total</col_1><col_2><body>59</col_2><col_3><body>47</col_3><col_4><body>78</col_4></row_14>
 </table>
 <text><location><page_8><loc_9><loc_44><loc_48><loc_51></location>Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .</text>
 <text><location><page_8><loc_9><loc_26><loc_48><loc_44></location>For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts.</text>
diff --git a/tests/data/groundtruth/docling_v2/2206.01062.json b/tests/data/groundtruth/docling_v2/2206.01062.json
index da1a7550..3a8242b7 100644
--- a/tests/data/groundtruth/docling_v2/2206.01062.json
+++ b/tests/data/groundtruth/docling_v2/2206.01062.json
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Rueschlikon, Switzerland dol@zurich.ibm.com"}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 172.54302978515625, "t": 599.942626953125, "r": 275.3072509765625, "b": 553.3746948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com", "text": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com"}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 336.6930236816406, "t": 599.942626953125, "r": 439.457275390625, "b": 553.3746948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com"}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 53.79803466796875, "t": 544.297119140625, "r": 111.94354248046875, "b": 533.9879760742188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "ABSTRACT", "text": "ABSTRACT", "level": 1}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.46699905395508, "t": 529.095458984375, "r": 295.5601806640625, "b": 257.7068176269531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1595]}], "orig": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 241.00308227539062, "r": 134.81988525390625, "b": 230.69398498535156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "CCS CONCEPTS", "text": "CCS CONCEPTS", "level": 1}, {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79798889160156, "t": 225.91700744628906, "r": 297.8529357910156, "b": 195.4988555908203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 170]}], "orig": "\u00b7 Information systems \u2192 Document structure ; \u00b7 Applied computing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;", "text": "\u00b7 Information systems \u2192 Document structure ; \u00b7 Applied computing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 157.60162353515625, "r": 295.11798095703125, "b": 119.2081069946289, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 116.91976928710938, "r": 197.8627471923828, "b": 110.43414306640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.31700134277344, "t": 108.18763732910156, "r": 186.74652099609375, "b": 101.67411041259766, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "\u00a9 2022 Copyright held by the owner/author(s).", "text": "\u00a9 2022 Copyright held by the owner/author(s)."}, {"self_ref": "#/texts/14", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.55400085449219, "t": 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"1."}, {"self_ref": "#/texts/37", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.63751, "t": 526.89484, "r": 387.98407, "b": 525.15582, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Turn the focusing knob away or toward", "text": "Turn the focusing knob away or toward"}, {"self_ref": "#/texts/38", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 526.06775, "r": 544.50403, "b": 524.02979, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "some of the controls in your vehicle. This chapter is a handy", "text": "some of the controls in your vehicle. This chapter is a handy"}, {"self_ref": "#/texts/39", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 525.28467, "r": 328.31903, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/40", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.03836, "t": 525.28467, "r": 354.21472, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "There are two objectives. The lower", "text": "There are two objectives. The lower"}, {"self_ref": "#/texts/41", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42023, "t": 525.28467, "r": 384.58948, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "you until a clear image is viewed.", "text": "you until a clear image is viewed."}, {"self_ref": "#/texts/42", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 524.02466, "r": 544.01343, "b": 521.98669, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "reference section that gives examples of the most common", "text": "reference section that gives examples of the most common"}, {"self_ref": "#/texts/43", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 523.67444, "r": 355.19193, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "magnification objective has a greater", "text": "magnification objective has a greater"}, {"self_ref": "#/texts/44", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42166, "t": 523.67444, "r": 359.78549, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/45", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.46741, "t": 523.67444, "r": 384.33441, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "If the image is unclear, adjust the", "text": "If the image is unclear, adjust the"}, {"self_ref": "#/texts/46", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 409.39554, "t": 523.01764, "r": 419.06677, "b": 521.58417, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Revolving Turret", "text": "Revolving Turret"}, {"self_ref": "#/texts/47", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 522.06421, "r": 345.80057, "b": 520.3252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "depth of field and view.", "text": "depth of field and view."}, {"self_ref": "#/texts/48", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 522.06421, "r": 384.61502, "b": 520.3252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "height of the elevator up or down,", "text": "height of the elevator up or down,"}, {"self_ref": "#/texts/49", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 521.98169, "r": 544.11987, "b": 519.94366, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "signs, signals and road markings that keep traffi c organized", "text": "signs, signals and road markings that keep traffi c organized"}, {"self_ref": "#/texts/50", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.37466, "t": 521.85925, "r": 479.14251999999993, "b": 519.82123, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "\u2022", "text": "\u2022"}, {"self_ref": "#/texts/51", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.91036999999994, "t": 521.85925, "r": 483.74963, "b": 519.82123, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Signs", "text": "Signs"}, {"self_ref": "#/texts/52", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 437.34607, "t": 520.86975, "r": 440.80496, "b": 519.43719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Stand", "text": "Stand"}, {"self_ref": "#/texts/53", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 520.45398, "r": 328.33862, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/54", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.06775, "t": 520.45398, "r": 352.39969, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "In order to observe the specimen", "text": "In order to observe the specimen"}, {"self_ref": "#/texts/55", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 520.45398, "r": 385.38922, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "then turn the focusing knob again.", "text": "then turn the focusing knob again."}, {"self_ref": "#/texts/56", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 519.9386, "r": 515.41071, "b": 517.90063, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "and flowing smoothly.", "text": "and flowing smoothly."}, {"self_ref": "#/texts/57", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 519.15283, "r": 492.31219, "b": 517.65112, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "- regulatory signs", "text": "- regulatory signs"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 518.84381, "r": 352.90042, "b": 517.10474, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "easily use the lower magnification", "text": "easily use the lower magnification"}, {"self_ref": "#/texts/59", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 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Then, by rotating the", "text": "objective first. Then, by rotating the"}, {"self_ref": "#/texts/60", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 517.23358, "r": 377.35046, "b": 515.49457, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "ZOOM MAGNIFICATION", "text": "ZOOM MAGNIFICATION"}, {"self_ref": "#/texts/61", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 516.85486, "r": 486.72598000000005, "b": 515.35321, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "- school,", "text": "- school,"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 408.24518, "t": 516.47327, "r": 414.4234, "b": 515.03979, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Objectives", "text": "Objectives"}, {"self_ref": "#/texts/63", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 515.62335, "r": 350.81885, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "case, the magnification can be", "text": "case, the magnification can be"}, {"self_ref": "#/texts/64", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 515.62335, "r": 359.89429, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/65", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.62988, "t": 515.62335, "r": 386.37589, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Turn the zoom magnification knob to", "text": "Turn the zoom magnification knob to"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 515.22028, "r": 492.93286000000006, "b": 513.18231, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "playground and", "text": "playground and"}, {"self_ref": "#/texts/67", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 514.65381, "r": 505.64642000000003, "b": 511.0643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Signs", "text": "Signs"}, {"self_ref": "#/texts/68", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 514.01312, "r": 335.46707, "b": 512.27411, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "changed.", "text": "changed."}, {"self_ref": "#/texts/69", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 514.01312, "r": 386.78732, "b": 512.27411, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "the desired magnification and field of", "text": "the desired magnification and field of"}, {"self_ref": "#/texts/70", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 513.17725, "r": 491.82938000000007, "b": 511.13925, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "crosswalk signs", "text": "crosswalk signs"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 408.00577, "t": 512.87421, "r": 411.42212, "b": 511.4407, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Stage", "text": "Stage"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.3895, "t": 512.87372, "r": 445.87192, "b": 511.44025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Coarse", "text": "Coarse"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 512.40295, "r": 364.16855, "b": 510.66391, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "view.", "text": "view."}, {"self_ref": "#/texts/74", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.3895, "t": 511.69391, "r": 448.22338999999994, "b": 510.2604099999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Adjustment", "text": "Adjustment"}, {"self_ref": "#/texts/75", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 404.07172, "t": 511.0855700000001, "r": 410.77707, "b": 509.6521, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Condenser", "text": "Condenser"}, {"self_ref": "#/texts/76", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 510.79272, "r": 354.57755, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "CHANGING THE INTERPUPILLARY", "text": "CHANGING THE INTERPUPILLARY"}, {"self_ref": "#/texts/77", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 510.79272, "r": 359.86777, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/78", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.59012, "t": 510.79272, "r": 387.31656, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "In most situations, it is recommended", "text": "In most situations, it is recommended"}, {"self_ref": "#/texts/79", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.3895, "t": 510.51407, "r": 444.40371999999996, "b": 509.08060000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Knob", "text": "Knob"}, {"self_ref": "#/texts/80", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 510.47241, "r": 491.00775000000004, "b": 508.97076, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "- lane use signs", "text": "- lane use signs"}, {"self_ref": "#/texts/81", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 510.17813, "r": 543.92957, "b": 508.14017, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "There are three ways to read signs: by their shape, colour and", "text": "There are three ways to read signs: by their shape, colour and"}, {"self_ref": "#/texts/82", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 404.07172, "t": 509.90576, "r": 409.2157, "b": 508.47226, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Focusing", "text": "Focusing"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 509.18249999999995, "r": 335.1752, "b": 507.44348, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "DISTANCE", "text": "DISTANCE"}, {"self_ref": "#/texts/84", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 509.18249999999995, "r": 381.56656, "b": 507.44348, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "that you focus at the lowest", "text": "that you focus at the lowest"}, {"self_ref": "#/texts/85", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 442.01610999999997, "t": 508.91351, "r": 444.8817399999999, "b": 507.48004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Fine", "text": "Fine"}, {"self_ref": "#/texts/86", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 404.07172, "t": 508.72592, "r": 407.08594, "b": 507.2924499999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Knob", "text": "Knob"}, {"self_ref": "#/texts/87", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 508.17444, "r": 493.32748, "b": 506.6727900000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "- turn control signs", "text": "- turn control signs"}, {"self_ref": "#/texts/88", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 508.1351, "r": 545.67834, "b": 506.09711, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "the messages printed on them. Understanding these three ways", "text": "the messages printed on them. Understanding these three ways"}, {"self_ref": "#/texts/89", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 442.01610999999997, "t": 507.7337, "r": 448.85001, "b": 506.30019999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Adjustment", "text": "Adjustment"}, {"self_ref": "#/texts/90", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 409.7164, "t": 507.59973, "r": 413.3768, "b": 506.16718, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Lamp", "text": "Lamp"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 507.5723, "r": 328.34784, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/92", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.08157, "t": 507.5723, "r": 354.76245, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "The distance between the observer's", "text": "The distance between the observer's"}, {"self_ref": "#/texts/93", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 507.5723, "r": 386.63403, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "magnification, then move to a higher", "text": "magnification, then move to a higher"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 442.01610999999997, "t": 506.55389, "r": 445.03033000000005, "b": 505.12039, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Knob", "text": "Knob"}, {"self_ref": "#/texts/95", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, 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"text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 505.96207, "r": 382.77115, "b": 504.22305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "magnification and re-focus as", "text": "magnification and re-focus as"}, {"self_ref": "#/texts/99", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 505.8765, "r": 490.4915199999999, "b": 504.37482, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "- parking signs", "text": "- parking signs"}, {"self_ref": "#/texts/100", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.84316999999993, "t": 505.09427, "r": 447.87585000000007, "b": 503.66174, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Stage Clip", "text": "Stage Clip"}, {"self_ref": "#/texts/101", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 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"charspan": [0, 10]}], "orig": "necessary.", "text": "necessary."}, {"self_ref": "#/texts/105", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 504.04901, "r": 513.31335, "b": 502.01105, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "that are new to you.", "text": "that are new to you."}, {"self_ref": "#/texts/106", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.84316999999993, "t": 503.6629, "r": 448.67252, "b": 502.23037999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Adjustment", "text": "Adjustment"}, {"self_ref": "#/texts/107", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 503.57852, "r": 491.17004000000003, "b": 502.07684, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "- reserved lane", "text": "- reserved 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"BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "FOCUSING", "text": "FOCUSING"}, {"self_ref": "#/texts/121", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 496.30078, "r": 381.74539, "b": 494.56177, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "DIOPTER RING ADJUSTMENT", "text": "DIOPTER RING ADJUSTMENT"}, {"self_ref": "#/texts/122", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 434.8712499999999, "t": 495.2847, "r": 438.53164999999996, "b": 493.85217, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Lamp", "text": "Lamp"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 494.69058, "r": 328.34314, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/124", 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40]}], "orig": "first while turning the focus knob until", "text": "first while turning the focus knob until"}, {"self_ref": "#/texts/143", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 488.24973, "r": 364.88672, "b": 486.51068, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "steps:", "text": "steps:"}, {"self_ref": "#/texts/144", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.32535, "t": 486.95709, "r": 435.93542, "b": 483.04427999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "MICROSCOPE USAGE", "text": "MICROSCOPE USAGE"}, {"self_ref": "#/texts/145", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 486.6395, "r": 354.46594, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "the image appears clear and sharp.", "text": "the image appears clear and sharp."}, {"self_ref": "#/texts/146", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 486.6395, "r": 359.95078, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "a.", "text": "a."}, {"self_ref": "#/texts/147", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 361.47699, "t": 486.6395, "r": 386.65988, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Observe an image through the left", "text": "Observe an image through the left"}, {"self_ref": "#/texts/148", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 485.9620100000001, "r": 489.99047999999993, "b": 484.46033, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "- railway signs", "text": "- railway signs"}, {"self_ref": "#/texts/149", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 485.0293, "r": 328.25488, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4.", "text": "4."}, {"self_ref": "#/texts/150", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 328.9407, "t": 485.0293, "r": 356.37335, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Rotate the right eyepiece ring until the", "text": "Rotate the right eyepiece ring until the"}, {"self_ref": "#/texts/151", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 485.0293, "r": 386.7634, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "eyepiece and bring a specific point", "text": "eyepiece and bring a specific point"}, {"self_ref": "#/texts/152", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.375, "t": 483.75211, "r": 479.1032400000001, "b": 481.71414, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "\u2022", "text": "\u2022"}, {"self_ref": "#/texts/153", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.83151, "t": 483.75211, "r": 484.92925999999994, "b": 481.71414, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Signals", "text": "Signals"}, {"self_ref": "#/texts/154", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 483.41907, "r": 355.38867, "b": 481.68005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "images in each eyepiece coincide and", "text": "images in each eyepiece coincide and"}, {"self_ref": "#/texts/155", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 483.41907, "r": 385.41354, "b": 481.68005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "into focus using the focus knob.", "text": "into focus using the focus knob."}, {"self_ref": "#/texts/156", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 481.80887, "r": 343.17249, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "are sharp and clear.", "text": "are sharp and clear."}, {"self_ref": "#/texts/157", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 481.80887, "r": 359.93304, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "b.", "text": "b."}, {"self_ref": "#/texts/158", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 361.44156, "t": 481.80887, "r": 382.56085, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "By turning the diopter ring", "text": "By turning the diopter ring"}, {"self_ref": "#/texts/159", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 481.64108, "r": 453.72171, "b": 479.46729, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "BARSKA Model AY11236 is a powerful fixed power compound", "text": "BARSKA Model AY11236 is a powerful fixed power compound"}, {"self_ref": "#/texts/160", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 481.04642, "r": 490.00091999999995, "b": 479.54474, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "- lane control", "text": "- lane control"}, {"self_ref": "#/texts/161", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 480.19864, "r": 385.4559, "b": 478.45963, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "adjustment for the left eyepiece,", "text": "adjustment for the left eyepiece,"}, {"self_ref": "#/texts/162", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 479.49414, "r": 453.09939999999995, "b": 477.32034, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "microscope designed for biological studies such as specimen", "text": "microscope designed for biological studies such as specimen"}, {"self_ref": "#/texts/163", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 479.4118000000001, "r": 485.95331, "b": 477.37384, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "signals", "text": "signals"}, {"self_ref": "#/texts/164", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 478.58844, "r": 344.13388, "b": 476.84940000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CHANGING THE BULB", "text": "CHANGING THE BULB"}, {"self_ref": "#/texts/165", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 478.58844, "r": 384.56122, "b": 476.84940000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "bring the same point into sharp", "text": "bring the same point into sharp"}, {"self_ref": "#/texts/166", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 477.3472, "r": 456.65246999999994, "b": 475.1734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "examination. It can also be used for examining bacteria and", "text": "examination. It can also be used for examining bacteria and"}, {"self_ref": "#/texts/167", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 476.97821000000005, "r": 328.37418, "b": 475.23920000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/168", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.11963, "t": 476.97821000000005, "r": 348.50162, "b": 475.23920000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "Disconnect the power cord.", "text": "Disconnect the power cord."}, {"self_ref": "#/texts/169", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 476.97821000000005, "r": 366.74371, "b": 475.23920000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "focus.", "text": "focus."}, {"self_ref": "#/texts/170", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 476.70621, "r": 489.29876999999993, "b": 475.20456, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "- traffic lights", "text": "- traffic lights"}, {"self_ref": "#/texts/171", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 475.36801, "r": 328.34061, "b": 473.62897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/172", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.06931, "t": 475.36801, "r": 353.11588, "b": 473.62897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "When the bulb is cool, remove the", "text": "When the bulb is cool, remove the"}, {"self_ref": "#/texts/173", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 475.36801, "r": 383.93884, "b": 473.62897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "c.Then bring the same point into", "text": "c.Then bring the same point into"}, {"self_ref": "#/texts/174", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 475.20023, "r": 456.73859000000004, "b": 473.02646, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "for general clinical and medical studies and other scientific uses.", "text": "for general clinical and medical studies and other scientific uses."}, {"self_ref": "#/texts/175", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.375, "t": 474.49634, "r": 479.18129999999996, "b": 472.4583400000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "\u2022", "text": "\u2022"}, {"self_ref": "#/texts/176", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.98761, "t": 474.49634, "r": 490.46960000000007, "b": 472.4583400000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Road markings", "text": "Road markings"}, {"self_ref": "#/texts/177", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88464, "t": 473.7577800000001, "r": 353.79517, "b": 472.0187700000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "oblique illuminator cap and remove", "text": "oblique illuminator cap and remove"}, {"self_ref": "#/texts/178", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 473.7577800000001, "r": 385.69241, "b": 472.0187700000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "focus through the right eyepiece", "text": "focus through the right eyepiece"}, {"self_ref": "#/texts/179", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 534.87561, "t": 473.62384, "r": 546.95142, "b": 471.80341, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "School zone signs", "text": "School zone signs"}, {"self_ref": "#/texts/180", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 518.66455, "t": 472.40854, "r": 529.80902, "b": 470.58809999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "Explains lane use", "text": "Explains lane use"}, {"self_ref": "#/texts/181", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88464, "t": 472.14757999999995, "r": 348.02094, "b": 470.40854, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "the halogen bulb with cap.", "text": "the halogen bulb with cap."}, {"self_ref": "#/texts/182", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 472.14757999999995, "r": 385.94861, "b": 470.40854, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "by turning the right diopter ring.", "text": "by turning the right diopter ring."}, {"self_ref": "#/texts/183", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 534.87561, "t": 471.9362499999999, "r": 545.05762, "b": 470.11581, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "are fl uorescent", "text": "are fl uorescent"}, {"self_ref": "#/texts/184", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 471.79062, "r": 489.26166000000006, "b": 470.28897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "- yellow lines", "text": "- yellow lines"}, {"self_ref": "#/texts/185", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.62399, "t": 471.57059, "r": 427.77472, "b": 467.65777999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "CONSTRUCTION", "text": "CONSTRUCTION"}, {"self_ref": "#/texts/186", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 501.79385, "t": 470.81027, "r": 510.41632, "b": 468.98984, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Shows driving", "text": "Shows driving"}, {"self_ref": "#/texts/187", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88464, "t": 470.53735, "r": 328.37512, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/188", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.12036, "t": 470.53735, "r": 352.96808, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Replace with a new halogen bulb.", "text": "Replace with a new halogen bulb."}, {"self_ref": "#/texts/189", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 470.53735, "r": 385.54236, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "d.With more than one viewer, each", "text": "d.With more than one viewer, each"}, {"self_ref": "#/texts/190", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 534.87561, "t": 470.24866, "r": 543.32263, "b": 468.42822, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "yellow-green", "text": "yellow-green"}, {"self_ref": "#/texts/191", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 469.49268, "r": 488.59189, "b": 467.991, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "- white lines", "text": "- white lines"}, {"self_ref": "#/texts/192", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 501.79385, "t": 469.12268000000006, "r": 509.04268999999994, "b": 467.30224999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "regulations", "text": "regulations"}, {"self_ref": "#/texts/193", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 468.92715, "r": 328.36884, "b": 467.18811, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4.", "text": "4."}, {"self_ref": "#/texts/194", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.1102, "t": 468.92715, "r": 356.5412, "b": 467.18811, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Open the window in the base plate and", "text": "Open the window in the base plate and"}, {"self_ref": "#/texts/195", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 468.92715, "r": 382.98718, "b": 467.18811, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "viewer should note their own", "text": "viewer should note their own"}, {"self_ref": "#/texts/196", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 467.31692999999996, "r": 350.13828, "b": 465.57791, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "replace the halogen lamp or", "text": "replace the halogen lamp or"}, {"self_ref": "#/texts/197", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 467.31692999999996, "r": 385.06448, "b": 465.57791, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "diopter ring position for the left", "text": "diopter ring position for the left"}, {"self_ref": "#/texts/198", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 467.1947, "r": 491.17004000000003, "b": 465.69302, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "- reserved lane", "text": "- reserved lane"}, {"self_ref": "#/texts/199", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 465.70673, "r": 351.59677, "b": 463.96768, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "fluorescent lamp of transmitted", "text": "fluorescent lamp of transmitted"}, {"self_ref": "#/texts/200", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 465.70673, "r": 385.20682, "b": 463.96768, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "and right eyepieces, then before", "text": "and right eyepieces, then before"}, {"self_ref": "#/texts/201", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 465.56012, "r": 487.58978, "b": 463.52216, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "markings", "text": "markings"}, {"self_ref": "#/texts/202", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 465.53930999999994, "r": 456.02639999999997, "b": 463.36551, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "BARSKA Model AY11236 is a fixed power compound microscope.", "text": "BARSKA Model AY11236 is a fixed power compound microscope."}, {"self_ref": "#/texts/203", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 464.0965, "r": 336.89197, "b": 462.35748, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "illuminator.", "text": "illuminator."}, {"self_ref": "#/texts/204", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 464.0965, "r": 382.21964, "b": 462.35748, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "viewing set the diopter ring", "text": "viewing set the diopter ring"}, {"self_ref": "#/texts/205", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 463.3923300000001, "r": 455.42238999999995, "b": 461.2185400000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "It is constructed with two optical paths at the same angle. It is", "text": "It is constructed with two optical paths at the same angle. It is"}, {"self_ref": "#/texts/206", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 462.85449, "r": 491.75177, "b": 461.35284, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "- other markings", "text": "- other markings"}, {"self_ref": "#/texts/207", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 462.4863, "r": 382.63382, "b": 460.74725, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "adjustments to that setting.", "text": "adjustments to that setting."}, {"self_ref": "#/texts/208", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 461.24539, "r": 457.39844, "b": 459.07159, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "equipped with transmitted illumination. By using this instrument,", "text": "equipped with transmitted illumination. By using this instrument,"}, {"self_ref": "#/texts/209", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83005, "t": 460.42731000000003, "r": 351.16092, "b": 458.68829, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "USING THE VERTICAL TUBE -", "text": "USING THE VERTICAL TUBE -"}, {"self_ref": "#/texts/210", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 459.26587000000006, "r": 375.67661, "b": 457.52682000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CHANGING THE BULB", "text": "CHANGING THE BULB"}, {"self_ref": "#/texts/211", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 459.09845, "r": 453.97745, "b": 456.92464999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "the user can observe specimens at magnification from 40x to", "text": "the user can observe specimens at magnification from 40x to"}, {"self_ref": "#/texts/212", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83005, "t": 458.81708, "r": 348.30536, "b": 457.07806, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "MODELS AY11230/11234", "text": "MODELS AY11230/11234"}, {"self_ref": "#/texts/213", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 457.65564, "r": 359.90311, "b": 455.91663, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/214", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.64169, "t": 457.65564, "r": 385.75333, "b": 455.91663, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "Disconnect the power cord from the", "text": "Disconnect the power cord from the"}, {"self_ref": "#/texts/215", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 456.95148, "r": 454.70708999999994, "b": 454.77768, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "1000x by selecting the desired objective lens. Coarse and fine", "text": "1000x by selecting the desired objective lens. Coarse and fine"}, {"self_ref": "#/texts/216", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 456.04544, "r": 372.01416, "b": 454.3064, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "electrical outlet.", "text": "electrical outlet."}, {"self_ref": "#/texts/217", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83005, "t": 455.59561, "r": 329.05914, "b": 454.07394, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/218", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.67368, "t": 455.59561, "r": 349.95349, "b": 454.07394, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "The vertical tube can be used for", "text": "The vertical tube can be used for"}, {"self_ref": "#/texts/219", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 454.80453, "r": 458.90240000000006, "b": 452.63074, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "focus adjustments provide accuracy and image detail. The rotating", "text": "focus adjustments provide accuracy and image detail. The rotating"}, {"self_ref": "#/texts/220", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 454.43521, "r": 359.88327, "b": 452.6962, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/221", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.61191, "t": 454.43521, "r": 384.65726, "b": 452.6962, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "When the bulb is cool, remove the", "text": "When the bulb is cool, remove the"}, {"self_ref": "#/texts/222", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.11752, "t": 454.16412, "r": 353.57977, "b": 452.64248999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "instructional viewing or to photograph", "text": "instructional viewing or to photograph"}, {"self_ref": "#/texts/223", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 534.55847, "t": 454.11719, "r": 548.58453, "b": 452.2967499999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "Shows an action that", "text": "Shows an action that"}, {"self_ref": "#/texts/224", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 516.97748, "t": 453.93961, "r": 529.77484, "b": 452.11917000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "Shows a permitted", "text": "Shows a permitted"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 499.21862999999996, "t": 453.87228, "r": 512.62451, "b": 452.05185, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "Tells about motorist", "text": "Tells about motorist"}, {"self_ref": "#/texts/226", "parent": {"cref": 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"children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 406.24268, "r": 536.14716, "b": 404.12158, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a", "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a"}, {"self_ref": "#/texts/270", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 538.31085, "t": 406.24268, "r": 549.49921, "b": 404.12158, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "is placed on", "text": "is placed on"}, {"self_ref": "#/texts/271", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 404.61172, "r": 330.20337, "b": 402.35516000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/272", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.00137, "t": 404.61172, "r": 342.9715, "b": 402.35516000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "September 2012", "text": "September 2012"}, {"self_ref": "#/texts/273", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.4722, "t": 404.61172, "r": 374.88693, "b": 402.35516000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "Record date for 2012 fi nal dividend", "text": "Record date for 2012 fi nal dividend"}, {"self_ref": "#/texts/274", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 403.96399, "r": 547.58185, "b": 401.8429, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 119]}], "orig": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP."}, {"self_ref": "#/texts/275", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 402.06897, "r": 331.0173, "b": 399.81238, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "27", "text": "27"}, {"self_ref": "#/texts/276", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.82327, "t": 402.06897, "r": 343.91284, "b": 399.81238, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "September 2012", "text": "September 2012"}, {"self_ref": "#/texts/277", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.4722, "t": 402.06897, "r": 365.65988, "b": 399.81238, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Final dividend payable", "text": "Final dividend payable"}, {"self_ref": 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"prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 398.7871999999999, "r": 449.6163, "b": 396.66614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/282", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 450.1319, "t": 398.7871999999999, "r": 529.53082, "b": 396.66614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H", "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H"}, {"self_ref": "#/texts/283", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 532.05829, "t": 398.7871999999999, "r": 550.42261, "b": 396.66614, 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"charspan": [0, 13]}], "orig": "February 2013", "text": "February 2013"}, {"self_ref": "#/texts/287", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 342.64841, "t": 396.81702, "r": 342.65811, "b": 395.50142999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/288", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 396.50851, "r": 455.74692, "b": 394.38745, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "minima.", "text": "minima."}, {"self_ref": "#/texts/289", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.47144, "t": 394.44287, "r": 385.93265, "b": 392.18631, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "2013 full year results and fi nal dividend announced", "text": "2013 full year results and fi nal dividend announced"}, {"self_ref": "#/texts/290", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40491, "t": 394.44250000000005, "r": 331.02695, "b": 392.18594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "28", "text": "28"}, {"self_ref": "#/texts/291", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.83795, "t": 394.44250000000005, "r": 340.75909, "b": 392.18594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "August 2013", "text": "August 2013"}, {"self_ref": "#/texts/292", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 341.26437, "t": 394.2746, "r": 341.27408, "b": 392.95905, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/293", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": 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Place, Sydney"}, {"self_ref": "#/texts/296", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 385.62143, "r": 369.65308, "b": 383.36486999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "commencing at 10.30am on Tuesday 23 October 2012.", "text": "commencing at 10.30am on Tuesday 23 October 2012."}, {"self_ref": "#/texts/297", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 501.13077, "t": 382.74457, "r": 551.16101, "b": 380.8356, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 107]}], "orig": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V", "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V"}, {"self_ref": "#/texts/298", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 501.13077, "t": 380.69376, "r": 505.2477999999999, "b": 378.78479, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Table", "text": "Table"}, {"self_ref": "#/texts/299", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 376.40451, "r": 496.2829, "b": 374.49554, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H", "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H"}, {"self_ref": "#/texts/300", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.4054, "t": 372.06876, "r": 384.19696, "b": 360.90588, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Looking back on", "text": "Looking back on"}, {"self_ref": "#/texts/301", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 371.81198, "r": 469.35599, "b": 369.26669, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "AIRPORT SKETCH", "text": "AIRPORT SKETCH"}, {"self_ref": "#/texts/302", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 444.56319999999994, "t": 369.15131, "r": 446.25998, "b": 320.12872, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms"}, {"self_ref": "#/texts/303", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 366.91092, "r": 525.93616, "b": 364.78983, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related", "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related"}, {"self_ref": "#/texts/304", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 364.6322, "r": 522.0343, "b": 362.51114, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-"}, {"self_ref": "#/texts/305", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 362.35352, "r": 524.67151, "b": 360.23245, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "lot recognition of the airport from the air and to provide some information to aid on ground", "text": "lot recognition of the airport from the air and to provide some information to aid on ground"}, {"self_ref": "#/texts/306", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.4054, "t": 361.89621, "r": 372.16626, "b": 350.73331, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "175 years of", "text": "175 years of"}, {"self_ref": "#/texts/307", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 360.07485999999994, "r": 527.172, "b": 357.95377, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway", "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway"}, {"self_ref": "#/texts/308", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 357.79617, "r": 502.39545, "b": 355.67508, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "dimensions (length and width) are shown for all active runways.", "text": "dimensions (length and width) are shown for all active runways."}, {"self_ref": "#/texts/309", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 353.2388000000001, "r": 512.92676, "b": 351.11771000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text": "Runway(s) are depicted based on what type and construction of the runway."}, {"self_ref": "#/texts/310", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.4054, "t": 351.72363000000007, "r": 385.3981, "b": 340.56076, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "looking forward.", "text": "looking forward."}, {"self_ref": "#/texts/311", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 347.92999, "r": 460.02307, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Hard Surface", "text": "Hard Surface"}, {"self_ref": "#/texts/312", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 347.92999, "r": 473.98819, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Other Than", "text": "Other Than"}, {"self_ref": "#/texts/313", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.91357, "t": 347.92999, "r": 489.45648, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Metal Surface", "text": "Metal Surface"}, {"self_ref": "#/texts/314", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 493.06420999999995, "t": 347.92999, "r": 505.03076, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Closed Runway", "text": "Closed Runway"}, {"self_ref": "#/texts/315", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 509.5809, "t": 347.92999, "r": 524.30237, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Under Construction", "text": "Under Construction"}, {"self_ref": "#/texts/316", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 345.87915, "r": 474.96744, "b": 343.97021, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Hard Surface", "text": "Hard Surface"}, {"self_ref": "#/texts/317", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 337.18793, "r": 458.31406, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Stopways,", "text": "Stopways,"}, {"self_ref": "#/texts/318", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 337.18793, "r": 472.87732, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Displaced", "text": "Displaced"}, {"self_ref": "#/texts/319", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.91357, "t": 337.18793, "r": 483.61584, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Closed", "text": "Closed"}, {"self_ref": "#/texts/320", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 493.06420999999995, "t": 337.18793, "r": 504.20648, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Water Runway", "text": "Water Runway"}, {"self_ref": "#/texts/321", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 335.13712, "r": 461.92083999999994, "b": 333.22814999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Taxiways, Park-", "text": "Taxiways, Park-"}, {"self_ref": "#/texts/322", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 335.13712, "r": 472.49792, "b": 333.22814999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Threshold", "text": "Threshold"}, {"self_ref": "#/texts/323", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.91357, "t": 335.13712, "r": 486.60754000000003, "b": 333.22814999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Pavement", "text": "Pavement"}, {"self_ref": "#/texts/324", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 333.08627, "r": 457.08014, "b": 331.17731000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "ing Areas", "text": "ing Areas"}, {"self_ref": "#/texts/325", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 331.46945000000005, "r": 379.25955, "b": 326.45493, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Ye s te rd ay", "text": "Ye s te rd ay"}, {"self_ref": "#/texts/326", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 325.2843, "r": 391.38229, "b": 323.02777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Established in Sydney in 1837, and then", "text": "Established in Sydney in 1837, and then"}, {"self_ref": "#/texts/327", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 323.25076, "r": 395.01788, "b": 320.99423, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 42]}], "orig": "known as The Australian Gas Light Company,", "text": "known as The Australian Gas Light Company,"}, {"self_ref": "#/texts/328", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 322.67026, "r": 548.59674, "b": 320.54919, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 110]}], "orig": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-"}, {"self_ref": "#/texts/329", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 321.21719, "r": 394.08322, "b": 318.96066, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "the AGL business has an established history", "text": "the AGL business has an established history"}, {"self_ref": "#/texts/330", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 320.39157, "r": 500.08181999999994, "b": 318.27051, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "sions, runway slope, arresting gear, and displaced threshold.", "text": "sions, runway slope, arresting gear, and displaced threshold."}, {"self_ref": "#/texts/331", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 319.18365, "r": 390.60727, "b": 316.92712, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "and reputation for serving the gas and", "text": "and reputation for serving the gas and"}, {"self_ref": "#/texts/332", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 317.15012, "r": 393.49612, "b": 314.89355, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "electricity needs of Australian households.", "text": "electricity needs of Australian households."}, {"self_ref": "#/texts/333", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 315.83423, "r": 449.59933000000007, "b": 313.71313, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/334", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 450.09796, "t": 315.83423, "r": 547.82562, "b": 313.71313, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 195]}], "orig": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L", "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L"}, {"self_ref": "#/texts/335", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 547.82623, "t": 315.83423, "r": 548.45862, "b": 313.71313, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "-", "text": "-"}, {"self_ref": "#/texts/336", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 315.11655, "r": 394.11481, "b": 312.86002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "In 1841, when AGL supplied the gas to light", "text": "In 1841, when AGL supplied the gas to light"}, {"self_ref": "#/texts/337", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 313.55554, "r": 470.52609000000007, "b": 311.43445, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "pads may also be shown.", "text": "pads may also be shown."}, {"self_ref": "#/texts/338", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 313.08301, "r": 393.75891, "b": 310.82648, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "the fi rst public street lamp, it was reported", "text": "the fi rst public street lamp, it was reported"}, {"self_ref": "#/texts/339", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 311.04947, "r": 390.4975, "b": 308.79291, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "in the Sydney Gazette as a \u201cwonderful", "text": "in the Sydney Gazette as a \u201cwonderful"}, {"self_ref": "#/texts/340", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 309.0159, "r": 395.70975, "b": 306.75937, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "achievement of scientifi c knowledge, assisted", "text": "achievement of scientifi c knowledge, assisted"}, {"self_ref": "#/texts/341", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 308.99817, "r": 493.37906000000004, "b": 306.87708, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q", "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q"}, {"self_ref": "#/texts/342", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 306.98236, "r": 394.27283, "b": 304.7258, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "by mechanical ingenuity.\u201d Within two years,", "text": "by mechanical ingenuity.\u201d Within two years,"}, {"self_ref": "#/texts/343", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 304.94879, "r": 396.65939, "b": 302.69226, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "165 gas lamps were lighting the City of Sydney.", "text": "165 gas lamps were lighting the City of Sydney."}, {"self_ref": "#/texts/344", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 304.4408, "r": 549.16168, "b": 302.3197, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 118]}], "orig": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone", "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone"}, {"self_ref": "#/texts/345", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 302.16211, "r": 546.90881, "b": 300.04102, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 119]}], "orig": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an", "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an"}, {"self_ref": "#/texts/346", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 299.88342, "r": 551.80023, "b": 297.76233, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 189]}], "orig": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I", "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I"}, {"self_ref": "#/texts/347", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 297.60474, "r": 505.85068000000007, "b": 295.48364, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "the landing surface. Circling only approaches will not show a TDZE.", "text": "the landing surface. Circling only approaches will not show a TDZE."}, {"self_ref": "#/texts/348", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.80661000000003, "t": 276.05629999999996, "r": 502.08792, "b": 272.98235999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "114", "text": "114"}, {"self_ref": "#/texts/349", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 199.53408813476562, "r": 379.82049560546875, "b": 189.22499084472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "KEYWORDS", "text": "KEYWORDS", "level": 1}, {"self_ref": "#/texts/350", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 184.3324432373047, "r": 559.1859741210938, "b": 164.9988250732422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning"}, {"self_ref": "#/texts/351", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 317.65997314453125, "t": 151.94566345214844, "r": 404.6536560058594, "b": 144.41390991210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "ACM Reference Format:", "text": "ACM Reference Format:", "level": 1}, {"self_ref": "#/texts/352", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 141.88003540039062, "r": 559.5494995117188, "b": 84.62297058105469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 374]}], "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043"}, {"self_ref": "#/texts/353", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/354", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 53.79800033569336, "t": 706.14013671875, "r": 156.52899169921875, "b": 695.8309936523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "1 INTRODUCTION", "text": "1 INTRODUCTION", "level": 1}, {"self_ref": "#/texts/355", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.52899932861328, "t": 681.0164794921875, "r": 303.0169677734375, "b": 563.0528564453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 702]}], "orig": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1.", "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"self_ref": "#/texts/356", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.52899932861328, "t": 560.4684448242188, "r": 295.5641174316406, "b": 289.0808410644531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1580]}], "orig": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5.", "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"self_ref": "#/texts/357", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.59199905395508, "t": 286.4964599609375, "r": 295.56396484375, "b": 212.36782836914062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 462]}], "orig": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:", "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"self_ref": "#/texts/358", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 207.41844177246094, "r": 295.5616455078125, "b": 177.12582397460938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 149]}], "orig": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/359", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 174.54144287109375, "r": 294.2625427246094, "b": 155.20883178710938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 109]}], "orig": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/360", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 152.62445068359375, "r": 294.6838073730469, "b": 122.33183288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 180]}], "orig": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/361", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 119.7474365234375, "r": 295.56439208984375, "b": 100.41383361816406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 115]}], "orig": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/362", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 53.672000885009766, "t": 89.77363586425781, "r": 216.02749633789062, "b": 83.2601089477539, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"self_ref": "#/texts/363", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 342.0950012207031, "t": 704.636474609375, "r": 558.4320068359375, "b": 685.3028564453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 86]}], "orig": "This enables experimentation with annotation uncertainty and quality control analysis.", "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"self_ref": "#/texts/364", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 328.8650207519531, "t": 682.718505859375, "r": 559.7210083007812, "b": 630.5088500976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 280]}], "orig": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/365", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.62298583984375, "t": 624.0244750976562, "r": 559.1903076171875, "b": 571.8138427734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 297]}], "orig": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns.", "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"self_ref": "#/texts/366", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.7309875488281, "t": 569.2294311523438, "r": 559.5819702148438, "b": 484.142822265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 506]}], "orig": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery.", "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"self_ref": "#/texts/367", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 470.7911071777344, "r": 421.7441101074219, "b": 460.4820251464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "2 RELATED WORK", "text": "2 RELATED WORK", "level": 1}, {"self_ref": "#/texts/368", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.5249938964844, "t": 445.6674499511719, "r": 559.7161254882812, "b": 327.7038269042969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 655]}], "orig": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16].", "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"self_ref": "#/texts/369", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 325.1194763183594, "r": 559.1864624023438, "b": 240.03182983398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 500]}], "orig": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish.", "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"self_ref": "#/texts/370", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 226.6800994873047, "r": 477.4568786621094, "b": 216.37100219726562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "3 THE DOCLAYNET DATASET", "text": "3 THE DOCLAYNET DATASET", "level": 1}, {"self_ref": "#/texts/371", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 201.5564422607422, "r": 559.7131958007812, "b": 116.46983337402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 522]}], "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4.", "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"self_ref": "#/texts/372", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 113.88543701171875, "r": 558.2041015625, "b": 83.59282684326172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 186]}], "orig": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents", "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}, {"self_ref": "#/texts/373", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/374", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/375", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 555.885009765625, "r": 294.0437316894531, "b": 536.4527587890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 69]}], "orig": "Figure 2: Distribution of DocLayNet pages across document categories.", "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"self_ref": "#/texts/376", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 207.13306, "t": 698.8423499999999, "r": 237.64882999999998, "b": 690.31531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Scientific", "text": "Scientific"}, {"self_ref": "#/texts/377", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 88.288223, "t": 677.6452600000001, "r": 118.80401, "b": 669.1182300000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Financial", "text": "Financial"}, {"self_ref": "#/texts/378", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 184.40349, "t": 673.31793, "r": 199.66519, "b": 664.79089, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "17%", "text": "17%"}, {"self_ref": "#/texts/379", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 136.24422, "t": 661.75592, "r": 151.50592, "b": 653.22888, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "32%", "text": "32%"}, {"self_ref": "#/texts/380", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 237.11293, "t": 658.91284, "r": 262.97623, "b": 650.3858, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Patents", "text": "Patents"}, {"self_ref": "#/texts/381", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 202.87892, "t": 651.53821, "r": 213.89999, "b": 643.01117, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "8%", "text": "8%"}, {"self_ref": "#/texts/382", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 139.6235, "t": 621.77252, "r": 150.64458, "b": 613.24548, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "6%", "text": "6%"}, {"self_ref": "#/texts/383", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 194.40683, "t": 620.87854, "r": 209.66853, "b": 612.3515, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "21%", "text": "21%"}, {"self_ref": "#/texts/384", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 157.43983, "t": 608.22192, "r": 172.70154, "b": 599.69489, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "16%", "text": "16%"}, {"self_ref": "#/texts/385", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 93.973373, "t": 604.34235, "r": 121.11515, "b": 595.81531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Tenders", "text": "Tenders"}, {"self_ref": "#/texts/386", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 225.47252, "t": 602.70343, "r": 254.29510000000002, "b": 594.17639, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Manuals", "text": "Manuals"}, {"self_ref": "#/texts/387", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 139.88339, "t": 579.49963, "r": 157.68491, "b": 570.9726, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Laws", "text": "Laws"}, {"self_ref": "#/texts/388", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 510.19647216796875, "r": 294.2738342285156, "b": 425.1098327636719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 513]}], "orig": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\".", "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\"."}, {"self_ref": "#/texts/389", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.57400131225586, "t": 422.52545166015625, "r": 295.5604553222656, "b": 282.6438293457031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 810]}], "orig": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes.", "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"self_ref": "#/texts/390", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.46699905395508, "t": 280.0594482421875, "r": 295.5615539550781, "b": 184.01382446289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 535]}], "orig": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features.", "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"self_ref": "#/texts/391", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 181.429443359375, "r": 295.56396484375, "b": 107.30182647705078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 413]}], "orig": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions.", "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"self_ref": "#/texts/392", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 90.34363555908203, "r": 195.78997802734375, "b": 83.83010864257812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}, {"self_ref": "#/texts/393", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.62298583984375, "t": 704.636474609375, "r": 559.1918334960938, "b": 630.5088500976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 435]}], "orig": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5.", "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"self_ref": "#/texts/394", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.9549865722656, "t": 627.9244384765625, "r": 558.4381103515625, "b": 520.9197998046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 645]}], "orig": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames.", "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"self_ref": "#/texts/395", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 316.9419860839844, "t": 518.33544921875, "r": 559.7215576171875, "b": 203.11082458496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1854]}], "orig": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4.", "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"self_ref": "#/texts/396", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 317.9549865722656, "t": 185.15008544921875, "r": 470.2132568359375, "b": 174.8409881591797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "4 ANNOTATION CAMPAIGN", "text": "4 ANNOTATION CAMPAIGN", "level": 1}, {"self_ref": "#/texts/397", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.6860046386719, "t": 160.0264434814453, "r": 559.7138061523438, "b": 85.8978271484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 457]}], "orig": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,", "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}, {"self_ref": "#/texts/398", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/399", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.50199890136719, "t": 707.0450439453125, "r": 558.4896850585938, "b": 676.65380859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 348]}], "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"self_ref": "#/texts/400", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 237.99000549316406, "r": 295.64874267578125, "b": 185.68075561523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 281]}], "orig": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"self_ref": "#/texts/401", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 53.46699905395508, "t": 157.7084503173828, "r": 294.0474548339844, "b": 116.45683288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.", "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"self_ref": "#/texts/402", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 113.989013671875, "r": 295.5584411621094, "b": 83.57982635498047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 193]}], "orig": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources", "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"self_ref": "#/texts/403", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 479.92047119140625, "r": 559.1853637695312, "b": 416.7518310546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 376]}], "orig": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.", "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"self_ref": "#/texts/404", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 414.1674499511719, "r": 559.7130737304688, "b": 285.2448425292969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 746]}], "orig": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.", "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"self_ref": "#/texts/405", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.62298583984375, "t": 282.7770080566406, "r": 559.7176513671875, "b": 98.9438247680664, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1159]}], "orig": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on", "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"self_ref": "#/texts/406", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 89.64663696289062, "r": 369.2456970214844, "b": 83.13311004638672, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "$^{3}$https://arxiv.org/", "text": "$^{3}$https://arxiv.org/"}, {"self_ref": "#/texts/407", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/408", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/409", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 704.636474609375, "r": 294.04541015625, "b": 685.2938842773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 135]}], "orig": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category.", "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"self_ref": "#/texts/410", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 682.7184448242188, "r": 295.5592346191406, "b": 542.8378295898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 812]}], "orig": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages.", "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"self_ref": "#/texts/411", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 540.2534790039062, "r": 295.56005859375, "b": 455.16583251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 465]}], "orig": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:", "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"self_ref": "#/texts/412", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 443.4874572753906, "r": 294.04620361328125, "b": 402.22686767578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 202]}], "orig": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/413", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70799255371094, "t": 399.6514892578125, "r": 295.563720703125, "b": 358.39984130859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 208]}], "orig": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/414", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 355.81549072265625, "r": 294.0472412109375, "b": 336.4728698730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "(3) For every Caption , there must be exactly one corresponding Picture or Table .", "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table .", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/415", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 333.8984680175781, "r": 294.0459899902344, "b": 314.5648193359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "(4) Connected sub-pictures are grouped together in one Picture object.", "text": "(4) Connected sub-pictures are grouped together in one Picture object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/416", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 311.98046875, "r": 264.5057067871094, "b": 303.59686279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "(5) Formula numbers are included in a Formula object.", "text": "(5) Formula numbers are included in a Formula object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/417", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.7080078125, "t": 301.021484375, "r": 294.0461730957031, "b": 270.72882080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 160]}], "orig": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/418", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.52899932861328, "t": 259.0494689941406, "r": 295.5625305175781, "b": 217.798828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 221]}], "orig": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference.", "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"self_ref": "#/texts/419", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 215.3310089111328, "r": 295.562255859375, "b": 86.29182434082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 792]}], "orig": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations", "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}, {"self_ref": "#/texts/420", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 317.9549865722656, "t": 318.5060119628906, "r": 559.8057861328125, "b": 288.11480712890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 173]}], "orig": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"self_ref": "#/texts/421", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 339.38269, "t": 706.80933, "r": 417.83722, "b": 699.716, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "Compliant with guidelines", "text": "Compliant with guidelines"}, {"self_ref": "#/texts/422", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 451.42834, "t": 706.80933, "r": 546.22913, "b": 699.716, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Plausible but invalid alternative", "text": "Plausible but invalid alternative"}, {"self_ref": "#/texts/423", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 693.65894, "r": 326.01498, "b": 687.74786, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/424", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00214, "t": 612.20703, "r": 416.20551, "b": 610.09027, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037"}, {"self_ref": "#/texts/425", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 605.00897, "r": 326.01498, "b": 599.09796, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/426", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00201, "t": 546.92615, "r": 416.20538, "b": 544.80939, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860"}, {"self_ref": "#/texts/427", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 538.45807, "r": 326.01498, "b": 532.547, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/428", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00201, "t": 432.87512, "r": 416.20538, "b": 430.75833, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4"}, {"self_ref": "#/texts/429", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 350.33701, "t": 427.14294, "r": 513.48035, "b": 420.04964999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Borderline case: Two guideline-compliant alternatives", "text": "Borderline case: Two guideline-compliant alternatives"}, {"self_ref": "#/texts/430", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 424.91504000000003, "r": 326.01498, "b": 419.004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "D", "text": "D"}, {"self_ref": "#/texts/431", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 400.12841796875, "t": 333.5567321777344, "r": 476.331787109375, "b": 331.43994140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"self_ref": "#/texts/432", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 317.62298583984375, "t": 266.5024719238281, "r": 558.204345703125, "b": 247.1688232421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 123]}], "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.", "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"self_ref": "#/texts/433", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 317.62298583984375, "t": 244.7010040283203, "r": 559.7149047851562, "b": 82.78482818603516, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 987]}], "orig": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted", "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}, {"self_ref": "#/texts/434", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/435", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 6, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.64874267578125, "b": 608.98291015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 489]}], "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.", "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset."}, {"self_ref": "#/texts/436", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.52899932861328, "t": 421.07244873046875, "r": 295.5561218261719, "b": 215.43682861328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1252]}], "orig": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.", "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"self_ref": "#/texts/437", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 53.79800033569336, "t": 203.87008666992188, "r": 147.4853515625, "b": 193.5609893798828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "5 EXPERIMENTS", "text": "5 EXPERIMENTS", "level": 1}, {"self_ref": "#/texts/438", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.48400115966797, "t": 178.74644470214844, "r": 295.4281005859375, "b": 82.7008285522461, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 584]}], "orig": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this", "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"self_ref": "#/texts/439", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 512.9840087890625, "r": 559.8057861328125, "b": 449.7158203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 329]}], "orig": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"self_ref": "#/texts/440", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 678.80737, "r": 337.56735, "b": 672.73328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "70", "text": "70"}, {"self_ref": "#/texts/441", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 652.5094, "r": 337.56735, "b": 646.4353, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "65", "text": "65"}, {"self_ref": "#/texts/442", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 322.92276, "t": 643.62311, "r": 328.99686, "b": 605.20782, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "mAP 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"100", "text": "100"}, {"self_ref": "#/texts/461", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 410.28143, "t": 538.19159, "r": 483.47278000000006, "b": 532.11749, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "% of DocLayNet training set", "text": "% of DocLayNet training set"}, {"self_ref": "#/texts/462", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 407.98846435546875, "r": 558.2041625976562, "b": 388.6548156738281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 102]}], "orig": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.", "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"self_ref": "#/texts/463", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.6409912109375, "t": 386.0704650878906, "r": 558.4364013671875, "b": 311.9428405761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16].", "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"self_ref": "#/texts/464", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 295.1781005859375, "r": 466.8532409667969, "b": 284.8690185546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Baselines for Object Detection", "text": "Baselines for Object Detection", "level": 1}, {"self_ref": "#/texts/465", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.7489929199219, "t": 279.9754638671875, "r": 558.4308471679688, "b": 85.2998275756836, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1146]}], "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.", "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}, {"self_ref": "#/texts/466", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/467", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/468", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.6486511230469, "b": 663.77685546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 205]}], "orig": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.", "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels."}, {"self_ref": "#/texts/469", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 317.65899658203125, "t": 705.1270141601562, "r": 559.8068237304688, "b": 663.7767944335938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 189]}], "orig": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.", "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement."}, {"self_ref": "#/texts/470", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 472.4300842285156, "r": 131.05624389648438, "b": 462.1210021972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Learning Curve", "text": "Learning Curve", "level": 1}, {"self_ref": "#/texts/471", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 52.78499984741211, "t": 457.22845458984375, "r": 295.558349609375, "b": 262.55181884765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1157]}], "orig": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.", "text": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"self_ref": "#/texts/472", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 249.49008178710938, "r": 164.3289794921875, "b": 239.1809844970703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Impact of Class Labels", "text": "Impact of Class Labels", "level": 1}, {"self_ref": "#/texts/473", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.46699905395508, "t": 234.2884521484375, "r": 295.5567932128906, "b": 83.44783020019531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 910]}], "orig": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of", "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"self_ref": "#/texts/474", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.6860046386719, "t": 460.5964660644531, "r": 559.5849609375, "b": 375.50982666015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 469]}], "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.", "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"self_ref": "#/texts/475", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.9549560546875, "t": 362.6051025390625, "r": 549.860595703125, "b": 352.2960205078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "Impact of Document Split in Train and Test Set", "text": "Impact of Document Split in Train and Test Set", "level": 1}, {"self_ref": "#/texts/476", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.62298583984375, "t": 347.4034729003906, "r": 559.7138061523438, "b": 196.5628204345703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 852]}], "orig": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided.", "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"self_ref": "#/texts/477", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.9549865722656, "t": 183.6580810546875, "r": 418.5477600097656, "b": 173.34898376464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Dataset Comparison", "text": "Dataset Comparison", "level": 1}, {"self_ref": "#/texts/478", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.6860046386719, "t": 168.45645141601562, "r": 559.1881713867188, "b": 83.35986328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 521]}], "orig": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,", "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}, {"self_ref": "#/texts/479", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/480", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.648681640625, "b": 641.85888671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 298]}], "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.", "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"self_ref": "#/texts/481", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 401.0794677734375, "r": 294.047119140625, "b": 348.85986328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 295]}], "orig": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .", "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"self_ref": "#/texts/482", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.46699905395508, "t": 346.28546142578125, "r": 295.55908203125, "b": 206.40382385253906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 793]}], "orig": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts.", "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"self_ref": "#/texts/483", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 186.9390869140625, "r": 156.00534057617188, "b": 176.62998962402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "Example Predictions", "text": "Example Predictions", "level": 1}, {"self_ref": "#/texts/484", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.52899932861328, "t": 171.7364501953125, "r": 295.5584411621094, "b": 86.64982604980469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 481]}], "orig": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence.", "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"self_ref": "#/texts/485", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 317.95501708984375, "t": 706.14013671875, "r": 405.7296142578125, "b": 695.8309936523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "6 CONCLUSION", "text": "6 CONCLUSION", "level": 1}, {"self_ref": "#/texts/486", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.9549865722656, "t": 690.9384765625, "r": 559.7137451171875, "b": 605.850830078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 507]}], "orig": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect.", "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"self_ref": "#/texts/487", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.6860046386719, "t": 603.2664794921875, "r": 559.717041015625, "b": 507.2208251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust.", "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"self_ref": "#/texts/488", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.62298583984375, "t": 504.636474609375, "r": 558.4346923828125, "b": 474.3438415527344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 188]}], "orig": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap.", "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"self_ref": "#/texts/489", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 317.9549865722656, "t": 456.9081115722656, "r": 387.3695983886719, "b": 446.5990295410156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "REFERENCES", "text": "REFERENCES", "level": 1}, {"self_ref": "#/texts/490", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 321.197998046875, "t": 443.29766845703125, "r": 558.2009887695312, "b": 420.8371276855469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 191]}], "orig": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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(A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes.", "text": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. 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Rueschlikon, Switzerland dol@zurich.ibm.com"}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 172.54302978515625, "t": 599.942626953125, "r": 275.3072509765625, "b": 553.3746948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com", "text": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com"}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 336.6930236816406, "t": 599.942626953125, "r": 439.457275390625, "b": 553.3746948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com"}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 53.79803466796875, "t": 544.297119140625, "r": 111.94354248046875, "b": 533.9879760742188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "ABSTRACT", "text": "ABSTRACT", "level": 1}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.46699905395508, "t": 529.095458984375, "r": 295.5601806640625, "b": 257.7068176269531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1595]}], "orig": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 241.00308227539062, "r": 134.81988525390625, "b": 230.69398498535156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "CCS CONCEPTS", "text": "CCS CONCEPTS", "level": 1}, {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79798889160156, "t": 225.91700744628906, "r": 297.8529357910156, "b": 195.4988555908203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 170]}], "orig": "\u00b7 Information systems \u2192 Document structure ; \u00b7 Applied computing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;", "text": "\u00b7 Information systems \u2192 Document structure ; \u00b7 Applied computing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 157.60162353515625, "r": 295.11798095703125, "b": 119.2081069946289, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 116.91976928710938, "r": 197.8627471923828, "b": 110.43414306640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.31700134277344, "t": 108.18763732910156, "r": 186.74652099609375, "b": 101.67411041259766, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "\u00a9 2022 Copyright held by the owner/author(s).", "text": "\u00a9 2022 Copyright held by the owner/author(s)."}, {"self_ref": "#/texts/14", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.55400085449219, "t": 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This chapter is a handy", "text": "some of the controls in your vehicle. This chapter is a handy"}, {"self_ref": "#/texts/39", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 525.28467, "r": 328.31903, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/40", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.03836, "t": 525.28467, "r": 354.21472, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "There are two objectives. The lower", "text": "There are two objectives. The lower"}, {"self_ref": "#/texts/41", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42023, "t": 525.28467, "r": 384.58948, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "you until a clear image is viewed.", "text": "you until a clear image is viewed."}, {"self_ref": "#/texts/42", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 524.02466, "r": 544.01343, "b": 521.98669, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "reference section that gives examples of the most common", "text": "reference section that gives examples of the most common"}, {"self_ref": "#/texts/43", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 523.67444, "r": 355.19193, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "magnification objective has a greater", "text": "magnification objective has a greater"}, {"self_ref": "#/texts/44", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42166, "t": 523.67444, "r": 359.78549, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/45", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.46741, "t": 523.67444, "r": 384.33441, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "If the image is unclear, adjust the", "text": "If the image is unclear, adjust the"}, {"self_ref": "#/texts/46", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 409.39554, "t": 523.01764, "r": 419.06677, "b": 521.58417, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Revolving Turret", "text": "Revolving Turret"}, {"self_ref": "#/texts/47", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 522.06421, "r": 345.80057, "b": 520.3252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "depth of field and view.", "text": "depth of field and view."}, {"self_ref": "#/texts/48", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 522.06421, "r": 384.61502, "b": 520.3252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "height of the elevator up or down,", "text": "height of the elevator up or down,"}, {"self_ref": "#/texts/49", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 521.98169, "r": 544.11987, "b": 519.94366, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "signs, signals and road markings that keep traffi c organized", "text": "signs, signals and road markings that keep traffi c organized"}, {"self_ref": "#/texts/50", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.37466, "t": 521.85925, "r": 479.14251999999993, "b": 519.82123, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "\u2022", "text": "\u2022"}, {"self_ref": "#/texts/51", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.91036999999994, "t": 521.85925, "r": 483.74963, "b": 519.82123, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Signs", "text": "Signs"}, {"self_ref": "#/texts/52", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 437.34607, "t": 520.86975, "r": 440.80496, "b": 519.43719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Stand", "text": "Stand"}, {"self_ref": "#/texts/53", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 520.45398, "r": 328.33862, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/54", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.06775, "t": 520.45398, "r": 352.39969, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "In order to observe the specimen", "text": "In order to observe the specimen"}, {"self_ref": "#/texts/55", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 520.45398, "r": 385.38922, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "then turn the focusing knob again.", "text": "then turn the focusing knob again."}, {"self_ref": "#/texts/56", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 519.9386, "r": 515.41071, "b": 517.90063, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "and flowing smoothly.", "text": "and flowing smoothly."}, {"self_ref": "#/texts/57", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 519.15283, "r": 492.31219, "b": 517.65112, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "- regulatory signs", "text": "- regulatory signs"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 518.84381, "r": 352.90042, "b": 517.10474, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "easily use the lower magnification", "text": "easily use the lower magnification"}, {"self_ref": "#/texts/59", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 517.23358, "r": 354.59546, "b": 515.49457, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "objective first. Then, by rotating the", "text": "objective first. Then, by rotating the"}, {"self_ref": "#/texts/60", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 517.23358, "r": 377.35046, "b": 515.49457, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "ZOOM MAGNIFICATION", "text": "ZOOM MAGNIFICATION"}, {"self_ref": "#/texts/61", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 516.85486, "r": 486.72598000000005, "b": 515.35321, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "- school,", "text": "- school,"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 408.24518, "t": 516.47327, "r": 414.4234, "b": 515.03979, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Objectives", "text": "Objectives"}, {"self_ref": "#/texts/63", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 515.62335, "r": 350.81885, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "case, the magnification can be", "text": "case, the magnification can be"}, {"self_ref": "#/texts/64", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 515.62335, "r": 359.89429, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/65", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.62988, "t": 515.62335, "r": 386.37589, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Turn the zoom magnification knob to", "text": "Turn the zoom magnification knob to"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 515.22028, "r": 492.93286000000006, "b": 513.18231, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "playground and", "text": "playground and"}, {"self_ref": "#/texts/67", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 514.65381, "r": 505.64642000000003, "b": 511.0643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Signs", "text": "Signs"}, {"self_ref": "#/texts/68", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 514.01312, "r": 335.46707, "b": 512.27411, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "changed.", "text": "changed."}, {"self_ref": "#/texts/69", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 514.01312, "r": 386.78732, "b": 512.27411, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "the desired magnification and field of", "text": "the desired magnification and field of"}, {"self_ref": "#/texts/70", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 513.17725, "r": 491.82938000000007, "b": 511.13925, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "crosswalk signs", "text": "crosswalk signs"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 408.00577, "t": 512.87421, "r": 411.42212, "b": 511.4407, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Stage", "text": "Stage"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.3895, "t": 512.87372, "r": 445.87192, "b": 511.44025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Coarse", "text": "Coarse"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 512.40295, "r": 364.16855, "b": 510.66391, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "view.", "text": "view."}, {"self_ref": "#/texts/74", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.3895, "t": 511.69391, "r": 448.22338999999994, "b": 510.2604099999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Adjustment", "text": "Adjustment"}, {"self_ref": "#/texts/75", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 404.07172, "t": 511.0855700000001, "r": 410.77707, "b": 509.6521, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Condenser", "text": "Condenser"}, {"self_ref": "#/texts/76", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 510.79272, "r": 354.57755, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "CHANGING THE INTERPUPILLARY", "text": "CHANGING THE INTERPUPILLARY"}, {"self_ref": "#/texts/77", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 510.79272, "r": 359.86777, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/78", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.59012, "t": 510.79272, "r": 387.31656, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "In most situations, it is recommended", "text": "In most situations, it is recommended"}, {"self_ref": "#/texts/79", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.3895, "t": 510.51407, "r": 444.40371999999996, "b": 509.08060000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Knob", "text": "Knob"}, {"self_ref": "#/texts/80", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 510.47241, "r": 491.00775000000004, "b": 508.97076, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "- lane use signs", "text": "- lane use signs"}, {"self_ref": "#/texts/81", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 510.17813, "r": 543.92957, "b": 508.14017, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "There are three ways to read signs: by their shape, colour and", "text": "There are three ways to read signs: by their shape, colour and"}, {"self_ref": "#/texts/82", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 404.07172, "t": 509.90576, "r": 409.2157, "b": 508.47226, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Focusing", "text": "Focusing"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 509.18249999999995, "r": 335.1752, "b": 507.44348, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "DISTANCE", "text": "DISTANCE"}, {"self_ref": "#/texts/84", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 509.18249999999995, "r": 381.56656, "b": 507.44348, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "that you focus at the lowest", "text": "that you focus at the lowest"}, {"self_ref": "#/texts/85", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 442.01610999999997, "t": 508.91351, "r": 444.8817399999999, "b": 507.48004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Fine", "text": "Fine"}, {"self_ref": "#/texts/86", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 404.07172, "t": 508.72592, "r": 407.08594, "b": 507.2924499999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Knob", "text": "Knob"}, {"self_ref": "#/texts/87", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 508.17444, "r": 493.32748, "b": 506.6727900000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "- turn control signs", "text": "- turn control signs"}, {"self_ref": "#/texts/88", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 508.1351, "r": 545.67834, "b": 506.09711, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "the messages printed on them. Understanding these three ways", "text": "the messages printed on them. Understanding these three ways"}, {"self_ref": "#/texts/89", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 442.01610999999997, "t": 507.7337, "r": 448.85001, "b": 506.30019999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Adjustment", "text": "Adjustment"}, {"self_ref": "#/texts/90", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 409.7164, "t": 507.59973, "r": 413.3768, "b": 506.16718, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Lamp", "text": "Lamp"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 507.5723, "r": 328.34784, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/92", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.08157, "t": 507.5723, "r": 354.76245, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "The distance between the observer's", "text": "The distance between the observer's"}, {"self_ref": "#/texts/93", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 507.5723, "r": 386.63403, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "magnification, then move to a higher", "text": "magnification, then move to a higher"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 442.01610999999997, "t": 506.55389, "r": 445.03033000000005, "b": 505.12039, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Knob", "text": "Knob"}, {"self_ref": "#/texts/95", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 409.7164, "t": 506.16837, "r": 413.68201, "b": 504.73584, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "On/Off", "text": "On/Off"}, {"self_ref": "#/texts/96", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 506.09204, "r": 545.26471, "b": 504.05408, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "of classifying signs will help you figure out the meaning of signs", "text": "of classifying signs will help you figure out the meaning of signs"}, {"self_ref": "#/texts/97", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 505.96207, "r": 354.6499, "b": 504.22305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "pupils is the interpupillary distance.", "text": "pupils is the interpupillary distance."}, {"self_ref": "#/texts/98", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 505.96207, "r": 382.77115, "b": 504.22305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "magnification and re-focus as", "text": "magnification and re-focus as"}, {"self_ref": "#/texts/99", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 505.8765, "r": 490.4915199999999, "b": 504.37482, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "- parking signs", "text": "- parking signs"}, {"self_ref": "#/texts/100", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.84316999999993, "t": 505.09427, "r": 447.87585000000007, "b": 503.66174, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Stage Clip", "text": "Stage Clip"}, {"self_ref": "#/texts/101", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 409.7164, "t": 504.737, "r": 413.6337, "b": 503.30447, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Switch", "text": "Switch"}, {"self_ref": "#/texts/102", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 504.35187, "r": 328.25125, "b": 502.61282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/103", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 328.93671, "t": 504.35187, "r": 354.29825, "b": 502.61282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "To adjust the interpupillary distance", "text": "To adjust the interpupillary distance"}, {"self_ref": "#/texts/104", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 504.35187, "r": 367.98694, "b": 502.61282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "necessary.", "text": "necessary."}, {"self_ref": "#/texts/105", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 504.04901, "r": 513.31335, "b": 502.01105, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "that are new to you.", "text": "that are new to you."}, {"self_ref": "#/texts/106", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.84316999999993, "t": 503.6629, "r": 448.67252, "b": 502.23037999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Adjustment", "text": "Adjustment"}, {"self_ref": "#/texts/107", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 503.57852, "r": 491.17004000000003, "b": 502.07684, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "- reserved lane", "text": "- reserved 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"#/texts/111", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 501.94394000000005, "r": 484.77405000000005, "b": 499.90594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "signs", "text": "signs"}, {"self_ref": "#/texts/112", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 501.13144000000005, "r": 350.82028, "b": 499.3924, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "coincide with the image in the", "text": "coincide with the image in the"}, {"self_ref": "#/texts/113", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 501.13144000000005, "r": 388.03534, "b": 499.3924, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "at the same time, the diopter ring may", "text": "at the same time, the diopter ring may"}, {"self_ref": "#/texts/114", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 439.52039, "t": 499.81692999999996, "r": 443.08768, "b": 498.38439999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Power", "text": "Power"}, {"self_ref": "#/texts/115", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 499.52121, "r": 336.2067, "b": 497.7822, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "eyepiece.", "text": "eyepiece."}, {"self_ref": "#/texts/116", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 499.52121, "r": 373.13724, "b": 497.7822, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "need adjustment.", "text": "need adjustment."}, {"self_ref": "#/texts/117", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 499.23830999999996, "r": 490.83398, "b": 497.73666, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "- warning signs", "text": "- warning signs"}, {"self_ref": "#/texts/118", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 439.52039, "t": 498.38556, "r": 442.29575, "b": 496.95303, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Cord", "text": "Cord"}, {"self_ref": "#/texts/119", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 496.94037, "r": 491.62692, "b": 495.43869, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "- object markers", "text": "- object markers"}, {"self_ref": "#/texts/120", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 496.30078, "r": 335.3941, "b": 494.56177, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "FOCUSING", "text": "FOCUSING"}, {"self_ref": "#/texts/121", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 496.30078, "r": 381.74539, "b": 494.56177, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "DIOPTER RING ADJUSTMENT", "text": "DIOPTER RING ADJUSTMENT"}, {"self_ref": "#/texts/122", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 434.8712499999999, "t": 495.2847, "r": 438.53164999999996, "b": 493.85217, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Lamp", "text": "Lamp"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 494.69058, "r": 328.34314, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/124", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.07379, "t": 494.69058, "r": 353.18555, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Remove the lens protective cover.", "text": "Remove the lens protective cover."}, {"self_ref": "#/texts/125", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 494.69058, "r": 359.83682, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/126", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.54297, "t": 494.69058, "r": 388.08289, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "To adjust the eyepiece for viewing with", "text": "To adjust the eyepiece for viewing with"}, {"self_ref": "#/texts/127", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 494.6424, "r": 490.37341, "b": 493.1407500000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "- construction", "text": "- construction"}, {"self_ref": "#/texts/128", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 493.08035, "r": 328.35919, "b": 491.34134, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/129", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.0972, "t": 493.08035, "r": 353.45065, "b": 491.34134, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Place the specimen on the working", "text": "Place the specimen on the working"}, {"self_ref": "#/texts/130", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 493.08035, "r": 382.73251, "b": 491.34134, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "or without eyeglasses and for", "text": "or without eyeglasses and for"}, {"self_ref": "#/texts/131", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 493.00781, "r": 484.77405000000005, "b": 490.96985, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "signs", "text": "signs"}, {"self_ref": "#/texts/132", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 491.47015, "r": 333.32825, "b": 489.73110999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "stage.", "text": "stage."}, {"self_ref": "#/texts/133", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 491.47015, "r": 387.72266, "b": 489.73110999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "differences in acuity between the right", "text": "differences in acuity between the right"}, {"self_ref": "#/texts/134", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 422.10626, "t": 490.75809, "r": 434.62433000000004, "b": 489.01904, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Model AY11236", "text": "Model AY11236"}, {"self_ref": "#/texts/135", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 490.30219000000005, "r": 492.93912, "b": 488.80054, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "- information and", "text": "- information and"}, {"self_ref": "#/texts/136", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 489.85991999999993, "r": 328.31296, "b": 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508.53033000000005, "b": 487.10361, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Stop", "text": "Stop"}, {"self_ref": "#/texts/140", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 527.45502, "t": 488.74646, "r": 541.44678, "b": 486.92603, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Yield the right-of-way", "text": "Yield the right-of-way"}, {"self_ref": "#/texts/141", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 488.6676, "r": 493.00525, "b": 486.62964, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "destination signs", "text": "destination signs"}, {"self_ref": "#/texts/142", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 488.24973, "r": 355.96307, "b": 486.51068, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "first while turning the focus knob until", "text": "first while turning the focus knob until"}, {"self_ref": "#/texts/143", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 488.24973, "r": 364.88672, "b": 486.51068, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "steps:", "text": "steps:"}, {"self_ref": "#/texts/144", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.32535, "t": 486.95709, "r": 435.93542, "b": 483.04427999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "MICROSCOPE USAGE", "text": "MICROSCOPE USAGE"}, {"self_ref": "#/texts/145", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 486.6395, "r": 354.46594, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "the image appears clear and sharp.", "text": "the image appears clear and sharp."}, {"self_ref": "#/texts/146", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 486.6395, "r": 359.95078, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "a.", "text": "a."}, {"self_ref": "#/texts/147", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 361.47699, "t": 486.6395, "r": 386.65988, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Observe an image through the left", "text": "Observe an image through the left"}, {"self_ref": "#/texts/148", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 485.9620100000001, "r": 489.99047999999993, "b": 484.46033, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "- railway signs", "text": "- railway signs"}, {"self_ref": "#/texts/149", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 485.0293, "r": 328.25488, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4.", "text": "4."}, {"self_ref": "#/texts/150", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 328.9407, "t": 485.0293, "r": 356.37335, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Rotate the right eyepiece ring until the", "text": "Rotate the right eyepiece ring until the"}, {"self_ref": "#/texts/151", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 485.0293, "r": 386.7634, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "eyepiece and bring a specific point", "text": "eyepiece and bring a specific point"}, {"self_ref": 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"label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 483.41907, "r": 385.41354, "b": 481.68005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "into focus using the focus knob.", "text": "into focus using the focus knob."}, {"self_ref": "#/texts/156", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 481.80887, "r": 343.17249, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "are sharp and clear.", "text": "are sharp and clear."}, {"self_ref": "#/texts/157", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 481.80887, "r": 359.93304, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "b.", "text": "b."}, {"self_ref": "#/texts/158", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 361.44156, "t": 481.80887, "r": 382.56085, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "By turning the diopter ring", "text": "By turning the diopter ring"}, {"self_ref": "#/texts/159", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 481.64108, "r": 453.72171, "b": 479.46729, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "BARSKA Model AY11236 is a powerful fixed power compound", "text": "BARSKA Model AY11236 is a powerful fixed power compound"}, {"self_ref": "#/texts/160", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 481.04642, "r": 490.00091999999995, "b": 479.54474, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "- lane control", "text": "- lane control"}, {"self_ref": "#/texts/161", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 480.19864, "r": 385.4559, "b": 478.45963, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "adjustment for the left eyepiece,", "text": "adjustment for the left eyepiece,"}, {"self_ref": "#/texts/162", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 479.49414, "r": 453.09939999999995, "b": 477.32034, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "microscope designed for biological studies such as specimen", "text": "microscope designed for biological studies such as specimen"}, {"self_ref": "#/texts/163", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 479.4118000000001, "r": 485.95331, "b": 477.37384, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "signals", "text": "signals"}, {"self_ref": "#/texts/164", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 478.58844, "r": 344.13388, "b": 476.84940000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CHANGING THE BULB", "text": "CHANGING THE BULB"}, {"self_ref": "#/texts/165", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 478.58844, "r": 384.56122, "b": 476.84940000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "bring the same point into sharp", "text": "bring the same point into sharp"}, {"self_ref": "#/texts/166", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 477.3472, "r": 456.65246999999994, "b": 475.1734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "examination. It can also be used for examining bacteria and", "text": "examination. It can also be used for examining bacteria and"}, {"self_ref": "#/texts/167", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 476.97821000000005, "r": 328.37418, "b": 475.23920000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/168", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.11963, "t": 476.97821000000005, "r": 348.50162, "b": 475.23920000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "Disconnect the power cord.", "text": "Disconnect the power cord."}, {"self_ref": "#/texts/169", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 476.97821000000005, "r": 366.74371, "b": 475.23920000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "focus.", "text": "focus."}, 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"children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 475.36801, "r": 383.93884, "b": 473.62897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "c.Then bring the same point into", "text": "c.Then bring the same point into"}, {"self_ref": "#/texts/174", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 475.20023, "r": 456.73859000000004, "b": 473.02646, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "for general clinical and medical studies and other scientific uses.", "text": "for general clinical and medical studies and other scientific uses."}, {"self_ref": "#/texts/175", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.375, "t": 474.49634, "r": 479.18129999999996, "b": 472.4583400000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "\u2022", "text": "\u2022"}, {"self_ref": "#/texts/176", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.98761, "t": 474.49634, "r": 490.46960000000007, "b": 472.4583400000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Road markings", "text": "Road markings"}, {"self_ref": "#/texts/177", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88464, "t": 473.7577800000001, "r": 353.79517, "b": 472.0187700000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "oblique illuminator cap and remove", "text": "oblique illuminator cap and remove"}, {"self_ref": "#/texts/178", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 473.7577800000001, "r": 385.69241, "b": 472.0187700000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "focus through the right eyepiece", "text": "focus through the right 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"parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 472.14757999999995, "r": 385.94861, "b": 470.40854, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "by turning the right diopter ring.", "text": "by turning the right diopter ring."}, {"self_ref": "#/texts/183", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 534.87561, "t": 471.9362499999999, "r": 545.05762, "b": 470.11581, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "are fl uorescent", "text": "are fl uorescent"}, {"self_ref": "#/texts/184", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 471.79062, "r": 489.26166000000006, "b": 470.28897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "- yellow lines", "text": "- yellow lines"}, {"self_ref": "#/texts/185", "parent": {"cref": 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352.96808, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Replace with a new halogen bulb.", "text": "Replace with a new halogen bulb."}, {"self_ref": "#/texts/189", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 470.53735, "r": 385.54236, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "d.With more than one viewer, each", "text": "d.With more than one viewer, each"}, {"self_ref": "#/texts/190", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 534.87561, "t": 470.24866, "r": 543.32263, "b": 468.42822, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "yellow-green", "text": "yellow-green"}, {"self_ref": "#/texts/191", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 469.49268, "r": 488.59189, "b": 467.991, 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"text": "Open the window in the base plate and"}, {"self_ref": "#/texts/195", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 468.92715, "r": 382.98718, "b": 467.18811, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "viewer should note their own", "text": "viewer should note their own"}, {"self_ref": "#/texts/196", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 467.31692999999996, "r": 350.13828, "b": 465.57791, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "replace the halogen lamp or", "text": "replace the halogen lamp or"}, {"self_ref": "#/texts/197", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 467.31692999999996, "r": 385.06448, "b": 465.57791, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "diopter ring position for the left", "text": "diopter ring position for the left"}, {"self_ref": "#/texts/198", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 467.1947, "r": 491.17004000000003, "b": 465.69302, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "- reserved lane", "text": "- reserved lane"}, {"self_ref": "#/texts/199", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 465.70673, "r": 351.59677, "b": 463.96768, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "fluorescent lamp of transmitted", "text": "fluorescent lamp of transmitted"}, {"self_ref": "#/texts/200", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 465.70673, "r": 385.20682, "b": 463.96768, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "and right eyepieces, then before", "text": "and right eyepieces, then before"}, {"self_ref": "#/texts/201", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 465.56012, "r": 487.58978, "b": 463.52216, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "markings", "text": "markings"}, {"self_ref": "#/texts/202", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 465.53930999999994, "r": 456.02639999999997, "b": 463.36551, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "BARSKA Model AY11236 is a fixed power compound microscope.", "text": "BARSKA Model AY11236 is a fixed power compound microscope."}, {"self_ref": "#/texts/203", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 464.0965, "r": 336.89197, "b": 462.35748, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "illuminator.", "text": "illuminator."}, {"self_ref": "#/texts/204", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 464.0965, "r": 382.21964, "b": 462.35748, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "viewing set the diopter ring", "text": "viewing set the diopter ring"}, {"self_ref": "#/texts/205", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 463.3923300000001, "r": 455.42238999999995, "b": 461.2185400000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "It is constructed with two optical paths at the same angle. It is", "text": "It is constructed with two optical paths at the same angle. It is"}, {"self_ref": "#/texts/206", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 462.85449, "r": 491.75177, "b": 461.35284, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "- other markings", "text": "- other markings"}, {"self_ref": "#/texts/207", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 462.4863, "r": 382.63382, "b": 460.74725, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "adjustments to that setting.", "text": "adjustments to that setting."}, {"self_ref": "#/texts/208", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 461.24539, "r": 457.39844, "b": 459.07159, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "equipped with transmitted illumination. By using this instrument,", "text": "equipped with transmitted illumination. By using this instrument,"}, {"self_ref": "#/texts/209", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83005, "t": 460.42731000000003, "r": 351.16092, "b": 458.68829, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "USING THE VERTICAL TUBE -", "text": "USING THE VERTICAL TUBE -"}, {"self_ref": "#/texts/210", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 459.26587000000006, "r": 375.67661, "b": 457.52682000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CHANGING THE BULB", "text": "CHANGING THE BULB"}, {"self_ref": "#/texts/211", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 459.09845, "r": 453.97745, "b": 456.92464999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "the user can observe specimens at magnification from 40x to", "text": "the user can observe specimens at magnification from 40x to"}, {"self_ref": "#/texts/212", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83005, "t": 458.81708, "r": 348.30536, "b": 457.07806, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "MODELS AY11230/11234", "text": "MODELS AY11230/11234"}, {"self_ref": "#/texts/213", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 457.65564, "r": 359.90311, "b": 455.91663, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/214", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.64169, "t": 457.65564, "r": 385.75333, "b": 455.91663, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "Disconnect the power cord from the", "text": "Disconnect the power cord from the"}, {"self_ref": "#/texts/215", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 456.95148, "r": 454.70708999999994, "b": 454.77768, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "1000x by selecting the desired objective lens. Coarse and fine", "text": "1000x by selecting the desired objective lens. 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To indicate that the new criteria had been applied to a given procedure, a", "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a"}, {"self_ref": "#/texts/270", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 538.31085, "t": 406.24268, "r": 549.49921, "b": 404.12158, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "is placed on", "text": "is placed on"}, {"self_ref": "#/texts/271", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 404.61172, "r": 330.20337, "b": 402.35516000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/272", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.00137, "t": 404.61172, "r": 342.9715, "b": 402.35516000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "September 2012", "text": "September 2012"}, {"self_ref": "#/texts/273", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.4722, "t": 404.61172, "r": 374.88693, "b": 402.35516000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "Record date for 2012 fi nal dividend", "text": "Record date for 2012 fi nal dividend"}, {"self_ref": "#/texts/274", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 403.96399, "r": 547.58185, "b": 401.8429, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 119]}], "orig": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP."}, {"self_ref": "#/texts/275", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 402.06897, "r": 331.0173, "b": 399.81238, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "27", "text": "27"}, {"self_ref": "#/texts/276", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.82327, "t": 402.06897, "r": 343.91284, "b": 399.81238, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "September 2012", "text": "September 2012"}, {"self_ref": "#/texts/277", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.4722, "t": 402.06897, "r": 365.65988, "b": 399.81238, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Final dividend payable", "text": "Final dividend payable"}, {"self_ref": "#/texts/278", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 399.52618, "r": 330.98804, "b": 397.26962000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "23", "text": "23"}, {"self_ref": "#/texts/279", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.77936, "t": 399.52618, "r": 342.06674, "b": 397.26962000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "October 2012", "text": "October 2012"}, {"self_ref": "#/texts/280", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.4722, "t": 399.52618, "r": 367.22156, "b": 397.26962000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Annual General Meeting", "text": "Annual General Meeting"}, {"self_ref": "#/texts/281", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 398.7871999999999, "r": 449.6163, "b": 396.66614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/282", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 450.1319, "t": 398.7871999999999, "r": 529.53082, "b": 396.66614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H", "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H"}, {"self_ref": "#/texts/283", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 532.05829, "t": 398.7871999999999, "r": 550.42261, "b": 396.66614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "on the circling line of", "text": "on the circling line of"}, {"self_ref": "#/texts/284", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.47177, "t": 396.98526, "r": 386.25897, "b": 394.7287, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "2013 interim result and interim dividend announced", "text": "2013 interim result and interim dividend announced"}, {"self_ref": "#/texts/285", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 396.9834, "r": 330.99741, "b": 394.72687, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "27", "text": "27"}, {"self_ref": "#/texts/286", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.7934, "t": 396.9834, "r": 342.1416, "b": 394.72687, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "February 2013", "text": "February 2013"}, {"self_ref": "#/texts/287", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 342.64841, "t": 396.81702, "r": 342.65811, "b": 395.50142999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/288", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 396.50851, "r": 455.74692, "b": 394.38745, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "minima.", "text": "minima."}, {"self_ref": "#/texts/289", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.47144, "t": 394.44287, "r": 385.93265, "b": 392.18631, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "2013 full year results and fi nal dividend announced", "text": "2013 full year results and fi nal dividend announced"}, {"self_ref": "#/texts/290", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40491, "t": 394.44250000000005, "r": 331.02695, "b": 392.18594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "28", "text": "28"}, {"self_ref": "#/texts/291", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.83795, "t": 394.44250000000005, "r": 340.75909, "b": 392.18594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "August 2013", "text": "August 2013"}, {"self_ref": "#/texts/292", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 341.26437, "t": 394.2746, "r": 341.27408, "b": 392.95905, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/293", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 391.53845, "r": 329.87708, "b": 390.03412, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/294", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 330.34882, "t": 391.53845, "r": 358.65204, "b": 390.03412, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Indicative dates only, subject to change/Board confi rmation", "text": "Indicative dates only, subject to change/Board confi rmation"}, {"self_ref": "#/texts/295", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 387.65497, "r": 391.771, "b": 385.39844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 87]}], "orig": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney", "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney"}, {"self_ref": "#/texts/296", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 385.62143, "r": 369.65308, "b": 383.36486999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "commencing at 10.30am on Tuesday 23 October 2012.", "text": "commencing at 10.30am on Tuesday 23 October 2012."}, {"self_ref": "#/texts/297", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 501.13077, "t": 382.74457, "r": 551.16101, "b": 380.8356, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 107]}], "orig": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V", "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V"}, {"self_ref": "#/texts/298", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 501.13077, "t": 380.69376, "r": 505.2477999999999, "b": 378.78479, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Table", "text": "Table"}, {"self_ref": "#/texts/299", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 376.40451, "r": 496.2829, "b": 374.49554, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H", "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H"}, {"self_ref": "#/texts/300", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.4054, "t": 372.06876, "r": 384.19696, "b": 360.90588, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Looking back on", "text": "Looking back on"}, {"self_ref": "#/texts/301", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 371.81198, "r": 469.35599, "b": 369.26669, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "AIRPORT SKETCH", "text": "AIRPORT SKETCH"}, {"self_ref": "#/texts/302", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 444.56319999999994, "t": 369.15131, "r": 446.25998, "b": 320.12872, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms"}, {"self_ref": "#/texts/303", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 366.91092, "r": 525.93616, "b": 364.78983, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related", "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related"}, {"self_ref": "#/texts/304", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 364.6322, "r": 522.0343, "b": 362.51114, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-"}, {"self_ref": "#/texts/305", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 362.35352, "r": 524.67151, "b": 360.23245, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "lot recognition of the airport from the air and to provide some information to aid on ground", "text": "lot recognition of the airport from the air and to provide some information to aid on ground"}, {"self_ref": "#/texts/306", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.4054, "t": 361.89621, "r": 372.16626, "b": 350.73331, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "175 years of", "text": "175 years of"}, {"self_ref": "#/texts/307", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 360.07485999999994, "r": 527.172, "b": 357.95377, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway", "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway"}, {"self_ref": "#/texts/308", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 357.79617, "r": 502.39545, "b": 355.67508, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "dimensions (length and width) are shown for all active runways.", "text": "dimensions (length and width) are shown for all active runways."}, {"self_ref": "#/texts/309", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 353.2388000000001, "r": 512.92676, "b": 351.11771000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text": "Runway(s) are depicted based on what type and construction of the runway."}, {"self_ref": "#/texts/310", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.4054, "t": 351.72363000000007, "r": 385.3981, "b": 340.56076, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "looking forward.", "text": "looking forward."}, {"self_ref": "#/texts/311", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 347.92999, "r": 460.02307, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Hard Surface", "text": "Hard Surface"}, {"self_ref": "#/texts/312", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 347.92999, "r": 473.98819, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Other Than", "text": "Other Than"}, {"self_ref": "#/texts/313", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.91357, "t": 347.92999, "r": 489.45648, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Metal Surface", "text": "Metal Surface"}, {"self_ref": "#/texts/314", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 493.06420999999995, "t": 347.92999, "r": 505.03076, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Closed Runway", "text": "Closed Runway"}, {"self_ref": "#/texts/315", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 509.5809, "t": 347.92999, "r": 524.30237, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Under Construction", "text": "Under Construction"}, {"self_ref": "#/texts/316", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 345.87915, "r": 474.96744, "b": 343.97021, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Hard Surface", "text": "Hard Surface"}, {"self_ref": "#/texts/317", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 337.18793, "r": 458.31406, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Stopways,", "text": "Stopways,"}, {"self_ref": "#/texts/318", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 337.18793, "r": 472.87732, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Displaced", "text": "Displaced"}, {"self_ref": "#/texts/319", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.91357, "t": 337.18793, "r": 483.61584, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Closed", "text": "Closed"}, {"self_ref": "#/texts/320", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 493.06420999999995, "t": 337.18793, "r": 504.20648, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Water Runway", "text": "Water Runway"}, {"self_ref": "#/texts/321", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 335.13712, "r": 461.92083999999994, "b": 333.22814999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Taxiways, Park-", "text": "Taxiways, Park-"}, {"self_ref": "#/texts/322", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 335.13712, "r": 472.49792, "b": 333.22814999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Threshold", "text": "Threshold"}, {"self_ref": "#/texts/323", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.91357, "t": 335.13712, "r": 486.60754000000003, "b": 333.22814999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Pavement", "text": "Pavement"}, {"self_ref": "#/texts/324", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 333.08627, "r": 457.08014, "b": 331.17731000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "ing Areas", "text": "ing Areas"}, {"self_ref": "#/texts/325", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 331.46945000000005, "r": 379.25955, "b": 326.45493, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Ye s te rd ay", "text": "Ye s te rd ay"}, {"self_ref": "#/texts/326", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 325.2843, "r": 391.38229, "b": 323.02777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Established in Sydney in 1837, and then", "text": "Established in Sydney in 1837, and then"}, {"self_ref": "#/texts/327", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 323.25076, "r": 395.01788, "b": 320.99423, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 42]}], "orig": "known as The Australian Gas Light Company,", "text": "known as The Australian Gas Light Company,"}, {"self_ref": "#/texts/328", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 322.67026, "r": 548.59674, "b": 320.54919, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 110]}], "orig": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-"}, {"self_ref": "#/texts/329", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 321.21719, "r": 394.08322, "b": 318.96066, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "the AGL business has an established history", "text": "the AGL business has an established history"}, {"self_ref": "#/texts/330", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 320.39157, "r": 500.08181999999994, "b": 318.27051, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "sions, runway slope, arresting gear, and displaced threshold.", "text": "sions, runway slope, arresting gear, and displaced threshold."}, {"self_ref": "#/texts/331", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 319.18365, "r": 390.60727, "b": 316.92712, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "and reputation for serving the gas and", "text": "and reputation for serving the gas and"}, {"self_ref": "#/texts/332", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 317.15012, "r": 393.49612, "b": 314.89355, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "electricity needs of Australian households.", "text": "electricity needs of Australian households."}, {"self_ref": "#/texts/333", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 315.83423, "r": 449.59933000000007, "b": 313.71313, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/334", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 450.09796, "t": 315.83423, "r": 547.82562, "b": 313.71313, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 195]}], "orig": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L", "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L"}, {"self_ref": "#/texts/335", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 547.82623, "t": 315.83423, "r": 548.45862, "b": 313.71313, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "-", "text": "-"}, {"self_ref": "#/texts/336", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 315.11655, "r": 394.11481, "b": 312.86002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "In 1841, when AGL supplied the gas to light", "text": "In 1841, when AGL supplied the gas to light"}, {"self_ref": "#/texts/337", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 313.55554, "r": 470.52609000000007, "b": 311.43445, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "pads may also be shown.", "text": "pads may also be shown."}, {"self_ref": "#/texts/338", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 313.08301, "r": 393.75891, "b": 310.82648, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "the fi rst public street lamp, it was reported", "text": "the fi rst public street lamp, it was reported"}, {"self_ref": "#/texts/339", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 311.04947, "r": 390.4975, "b": 308.79291, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "in the Sydney Gazette as a \u201cwonderful", "text": "in the Sydney Gazette as a \u201cwonderful"}, {"self_ref": "#/texts/340", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 309.0159, "r": 395.70975, "b": 306.75937, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "achievement of scientifi c knowledge, assisted", "text": "achievement of scientifi c knowledge, assisted"}, {"self_ref": "#/texts/341", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 308.99817, "r": 493.37906000000004, "b": 306.87708, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q", "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q"}, {"self_ref": "#/texts/342", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 306.98236, "r": 394.27283, "b": 304.7258, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "by mechanical ingenuity.\u201d Within two years,", "text": "by mechanical ingenuity.\u201d Within two years,"}, {"self_ref": "#/texts/343", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 304.94879, "r": 396.65939, "b": 302.69226, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "165 gas lamps were lighting the City of Sydney.", "text": "165 gas lamps were lighting the City of Sydney."}, {"self_ref": "#/texts/344", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 304.4408, "r": 549.16168, "b": 302.3197, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 118]}], "orig": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone", "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone"}, {"self_ref": "#/texts/345", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 302.16211, "r": 546.90881, "b": 300.04102, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 119]}], "orig": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an", "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an"}, {"self_ref": "#/texts/346", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 299.88342, "r": 551.80023, "b": 297.76233, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 189]}], "orig": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I", "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I"}, {"self_ref": "#/texts/347", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 297.60474, "r": 505.85068000000007, "b": 295.48364, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "the landing surface. Circling only approaches will not show a TDZE.", "text": "the landing surface. Circling only approaches will not show a TDZE."}, {"self_ref": "#/texts/348", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.80661000000003, "t": 276.05629999999996, "r": 502.08792, "b": 272.98235999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "114", "text": "114"}, {"self_ref": "#/texts/349", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 199.53408813476562, "r": 379.82049560546875, "b": 189.22499084472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "KEYWORDS", "text": "KEYWORDS", "level": 1}, {"self_ref": "#/texts/350", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 184.3324432373047, "r": 559.1859741210938, "b": 164.9988250732422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning"}, {"self_ref": "#/texts/351", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 317.65997314453125, "t": 151.94566345214844, "r": 404.6536560058594, "b": 144.41390991210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "ACM Reference Format:", "text": "ACM Reference Format:", "level": 1}, {"self_ref": "#/texts/352", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 141.88003540039062, "r": 559.5494995117188, "b": 84.62297058105469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 374]}], "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043"}, {"self_ref": "#/texts/353", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/354", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 53.79800033569336, "t": 706.14013671875, "r": 156.52899169921875, "b": 695.8309936523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "1 INTRODUCTION", "text": "1 INTRODUCTION", "level": 1}, {"self_ref": "#/texts/355", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.52899932861328, "t": 681.0164794921875, "r": 303.0169677734375, "b": 563.0528564453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 702]}], "orig": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1.", "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"self_ref": "#/texts/356", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.52899932861328, "t": 560.4684448242188, "r": 295.5641174316406, "b": 289.0808410644531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1580]}], "orig": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5.", "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"self_ref": "#/texts/357", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.59199905395508, "t": 286.4964599609375, "r": 295.56396484375, "b": 212.36782836914062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 462]}], "orig": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:", "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"self_ref": "#/texts/358", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 207.41844177246094, "r": 295.5616455078125, "b": 177.12582397460938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 149]}], "orig": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/359", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 174.54144287109375, "r": 294.2625427246094, "b": 155.20883178710938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 109]}], "orig": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/360", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 152.62445068359375, "r": 294.6838073730469, "b": 122.33183288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 180]}], "orig": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/361", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 119.7474365234375, "r": 295.56439208984375, "b": 100.41383361816406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 115]}], "orig": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/362", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 53.672000885009766, "t": 89.77363586425781, "r": 216.02749633789062, "b": 83.2601089477539, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"self_ref": "#/texts/363", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 342.0950012207031, "t": 704.636474609375, "r": 558.4320068359375, "b": 685.3028564453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 86]}], "orig": "This enables experimentation with annotation uncertainty and quality control analysis.", "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"self_ref": "#/texts/364", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 328.8650207519531, "t": 682.718505859375, "r": 559.7210083007812, "b": 630.5088500976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 280]}], "orig": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/365", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.62298583984375, "t": 624.0244750976562, "r": 559.1903076171875, "b": 571.8138427734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 297]}], "orig": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns.", "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"self_ref": "#/texts/366", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.7309875488281, "t": 569.2294311523438, "r": 559.5819702148438, "b": 484.142822265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 506]}], "orig": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery.", "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"self_ref": "#/texts/367", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 470.7911071777344, "r": 421.7441101074219, "b": 460.4820251464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "2 RELATED WORK", "text": "2 RELATED WORK", "level": 1}, {"self_ref": "#/texts/368", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.5249938964844, "t": 445.6674499511719, "r": 559.7161254882812, "b": 327.7038269042969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 655]}], "orig": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16].", "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"self_ref": "#/texts/369", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 325.1194763183594, "r": 559.1864624023438, "b": 240.03182983398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 500]}], "orig": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish.", "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"self_ref": "#/texts/370", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 226.6800994873047, "r": 477.4568786621094, "b": 216.37100219726562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "3 THE DOCLAYNET DATASET", "text": "3 THE DOCLAYNET DATASET", "level": 1}, {"self_ref": "#/texts/371", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 201.5564422607422, "r": 559.7131958007812, "b": 116.46983337402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 522]}], "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4.", "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"self_ref": "#/texts/372", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 113.88543701171875, "r": 558.2041015625, "b": 83.59282684326172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 186]}], "orig": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents", "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}, {"self_ref": "#/texts/373", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/374", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/375", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 555.885009765625, "r": 294.0437316894531, "b": 536.4527587890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 69]}], "orig": "Figure 2: Distribution of DocLayNet pages across document categories.", "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"self_ref": "#/texts/376", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 207.13306, "t": 698.8423499999999, "r": 237.64882999999998, "b": 690.31531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Scientific", "text": "Scientific"}, {"self_ref": "#/texts/377", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 88.288223, "t": 677.6452600000001, "r": 118.80401, "b": 669.1182300000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Financial", "text": "Financial"}, {"self_ref": "#/texts/378", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 184.40349, "t": 673.31793, "r": 199.66519, "b": 664.79089, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "17%", "text": "17%"}, {"self_ref": "#/texts/379", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 136.24422, "t": 661.75592, "r": 151.50592, "b": 653.22888, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "32%", "text": "32%"}, {"self_ref": "#/texts/380", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 237.11293, "t": 658.91284, "r": 262.97623, "b": 650.3858, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Patents", "text": "Patents"}, {"self_ref": "#/texts/381", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 202.87892, "t": 651.53821, "r": 213.89999, "b": 643.01117, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "8%", "text": "8%"}, {"self_ref": "#/texts/382", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 139.6235, "t": 621.77252, "r": 150.64458, "b": 613.24548, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "6%", "text": "6%"}, {"self_ref": "#/texts/383", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 194.40683, "t": 620.87854, "r": 209.66853, "b": 612.3515, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "21%", "text": "21%"}, {"self_ref": "#/texts/384", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 157.43983, "t": 608.22192, "r": 172.70154, "b": 599.69489, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "16%", "text": "16%"}, {"self_ref": "#/texts/385", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 93.973373, "t": 604.34235, "r": 121.11515, "b": 595.81531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Tenders", "text": "Tenders"}, {"self_ref": "#/texts/386", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 225.47252, "t": 602.70343, "r": 254.29510000000002, "b": 594.17639, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Manuals", "text": "Manuals"}, {"self_ref": "#/texts/387", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 139.88339, "t": 579.49963, "r": 157.68491, "b": 570.9726, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Laws", "text": "Laws"}, {"self_ref": "#/texts/388", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 510.19647216796875, "r": 294.2738342285156, "b": 425.1098327636719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 513]}], "orig": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\".", "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\"."}, {"self_ref": "#/texts/389", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.57400131225586, "t": 422.52545166015625, "r": 295.5604553222656, "b": 282.6438293457031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 810]}], "orig": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes.", "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"self_ref": "#/texts/390", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.46699905395508, "t": 280.0594482421875, "r": 295.5615539550781, "b": 184.01382446289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 535]}], "orig": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features.", "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"self_ref": "#/texts/391", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 181.429443359375, "r": 295.56396484375, "b": 107.30182647705078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 413]}], "orig": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions.", "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"self_ref": "#/texts/392", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 90.34363555908203, "r": 195.78997802734375, "b": 83.83010864257812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}, {"self_ref": "#/texts/393", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.62298583984375, "t": 704.636474609375, "r": 559.1918334960938, "b": 630.5088500976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 435]}], "orig": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5.", "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"self_ref": "#/texts/394", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.9549865722656, "t": 627.9244384765625, "r": 558.4381103515625, "b": 520.9197998046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 645]}], "orig": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames.", "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"self_ref": "#/texts/395", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 316.9419860839844, "t": 518.33544921875, "r": 559.7215576171875, "b": 203.11082458496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1854]}], "orig": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4.", "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"self_ref": "#/texts/396", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 317.9549865722656, "t": 185.15008544921875, "r": 470.2132568359375, "b": 174.8409881591797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "4 ANNOTATION CAMPAIGN", "text": "4 ANNOTATION CAMPAIGN", "level": 1}, {"self_ref": "#/texts/397", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.6860046386719, "t": 160.0264434814453, "r": 559.7138061523438, "b": 85.8978271484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 457]}], "orig": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,", "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}, {"self_ref": "#/texts/398", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/399", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.50199890136719, "t": 707.0450439453125, "r": 558.4896850585938, "b": 676.65380859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 348]}], "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"self_ref": "#/texts/400", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 237.99000549316406, "r": 295.64874267578125, "b": 185.68075561523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 281]}], "orig": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"self_ref": "#/texts/401", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 53.46699905395508, "t": 157.7084503173828, "r": 294.0474548339844, "b": 116.45683288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.", "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"self_ref": "#/texts/402", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 113.989013671875, "r": 295.5584411621094, "b": 83.57982635498047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 193]}], "orig": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources", "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"self_ref": "#/texts/403", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 479.92047119140625, "r": 559.1853637695312, "b": 416.7518310546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 376]}], "orig": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.", "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"self_ref": "#/texts/404", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 414.1674499511719, "r": 559.7130737304688, "b": 285.2448425292969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 746]}], "orig": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.", "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"self_ref": "#/texts/405", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.62298583984375, "t": 282.7770080566406, "r": 559.7176513671875, "b": 98.9438247680664, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1159]}], "orig": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on", "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"self_ref": "#/texts/406", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 89.64663696289062, "r": 369.2456970214844, "b": 83.13311004638672, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "$^{3}$https://arxiv.org/", "text": "$^{3}$https://arxiv.org/"}, {"self_ref": "#/texts/407", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/408", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/409", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 704.636474609375, "r": 294.04541015625, "b": 685.2938842773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 135]}], "orig": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category.", "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"self_ref": "#/texts/410", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 682.7184448242188, "r": 295.5592346191406, "b": 542.8378295898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 812]}], "orig": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages.", "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"self_ref": "#/texts/411", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 540.2534790039062, "r": 295.56005859375, "b": 455.16583251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 465]}], "orig": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:", "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"self_ref": "#/texts/412", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 443.4874572753906, "r": 294.04620361328125, "b": 402.22686767578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 202]}], "orig": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/413", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70799255371094, "t": 399.6514892578125, "r": 295.563720703125, "b": 358.39984130859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 208]}], "orig": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/414", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 355.81549072265625, "r": 294.0472412109375, "b": 336.4728698730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "(3) For every Caption , there must be exactly one corresponding Picture or Table .", "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table .", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/415", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 333.8984680175781, "r": 294.0459899902344, "b": 314.5648193359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "(4) Connected sub-pictures are grouped together in one Picture object.", "text": "(4) Connected sub-pictures are grouped together in one Picture object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/416", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 311.98046875, "r": 264.5057067871094, "b": 303.59686279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "(5) Formula numbers are included in a Formula object.", "text": "(5) Formula numbers are included in a Formula object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/417", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.7080078125, "t": 301.021484375, "r": 294.0461730957031, "b": 270.72882080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 160]}], "orig": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/418", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.52899932861328, "t": 259.0494689941406, "r": 295.5625305175781, "b": 217.798828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 221]}], "orig": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference.", "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"self_ref": "#/texts/419", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 215.3310089111328, "r": 295.562255859375, "b": 86.29182434082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 792]}], "orig": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations", "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}, {"self_ref": "#/texts/420", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 317.9549865722656, "t": 318.5060119628906, "r": 559.8057861328125, "b": 288.11480712890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 173]}], "orig": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"self_ref": "#/texts/421", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 339.38269, "t": 706.80933, "r": 417.83722, "b": 699.716, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "Compliant with guidelines", "text": "Compliant with guidelines"}, {"self_ref": "#/texts/422", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 451.42834, "t": 706.80933, "r": 546.22913, "b": 699.716, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Plausible but invalid alternative", "text": "Plausible but invalid alternative"}, {"self_ref": "#/texts/423", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 693.65894, "r": 326.01498, "b": 687.74786, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/424", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00214, "t": 612.20703, "r": 416.20551, "b": 610.09027, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037"}, {"self_ref": "#/texts/425", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 605.00897, "r": 326.01498, "b": 599.09796, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/426", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00201, "t": 546.92615, "r": 416.20538, "b": 544.80939, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860"}, {"self_ref": "#/texts/427", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 538.45807, "r": 326.01498, "b": 532.547, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/428", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00201, "t": 432.87512, "r": 416.20538, "b": 430.75833, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4"}, {"self_ref": "#/texts/429", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 350.33701, "t": 427.14294, "r": 513.48035, "b": 420.04964999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Borderline case: Two guideline-compliant alternatives", "text": "Borderline case: Two guideline-compliant alternatives"}, {"self_ref": "#/texts/430", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 424.91504000000003, "r": 326.01498, "b": 419.004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "D", "text": "D"}, {"self_ref": "#/texts/431", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 400.12841796875, "t": 333.5567321777344, "r": 476.331787109375, "b": 331.43994140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"self_ref": "#/texts/432", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 317.62298583984375, "t": 266.5024719238281, "r": 558.204345703125, "b": 247.1688232421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 123]}], "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.", "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"self_ref": "#/texts/433", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 317.62298583984375, "t": 244.7010040283203, "r": 559.7149047851562, "b": 82.78482818603516, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 987]}], "orig": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted", "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}, {"self_ref": "#/texts/434", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/435", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 6, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.64874267578125, "b": 608.98291015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 489]}], "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.", "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset."}, {"self_ref": "#/texts/436", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.52899932861328, "t": 421.07244873046875, "r": 295.5561218261719, "b": 215.43682861328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1252]}], "orig": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.", "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"self_ref": "#/texts/437", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 53.79800033569336, "t": 203.87008666992188, "r": 147.4853515625, "b": 193.5609893798828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "5 EXPERIMENTS", "text": "5 EXPERIMENTS", "level": 1}, {"self_ref": "#/texts/438", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.48400115966797, "t": 178.74644470214844, "r": 295.4281005859375, "b": 82.7008285522461, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 584]}], "orig": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this", "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"self_ref": "#/texts/439", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 512.9840087890625, "r": 559.8057861328125, "b": 449.7158203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 329]}], "orig": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"self_ref": "#/texts/440", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 678.80737, "r": 337.56735, "b": 672.73328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "70", "text": "70"}, {"self_ref": "#/texts/441", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 652.5094, "r": 337.56735, "b": 646.4353, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "65", "text": "65"}, {"self_ref": "#/texts/442", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 322.92276, "t": 643.62311, "r": 328.99686, "b": 605.20782, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "mAP 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"100", "text": "100"}, {"self_ref": "#/texts/461", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 410.28143, "t": 538.19159, "r": 483.47278000000006, "b": 532.11749, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "% of DocLayNet training set", "text": "% of DocLayNet training set"}, {"self_ref": "#/texts/462", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 407.98846435546875, "r": 558.2041625976562, "b": 388.6548156738281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 102]}], "orig": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.", "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"self_ref": "#/texts/463", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.6409912109375, "t": 386.0704650878906, "r": 558.4364013671875, "b": 311.9428405761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16].", "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"self_ref": "#/texts/464", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 295.1781005859375, "r": 466.8532409667969, "b": 284.8690185546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Baselines for Object Detection", "text": "Baselines for Object Detection", "level": 1}, {"self_ref": "#/texts/465", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.7489929199219, "t": 279.9754638671875, "r": 558.4308471679688, "b": 85.2998275756836, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1146]}], "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.", "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}, {"self_ref": "#/texts/466", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/467", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/468", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.6486511230469, "b": 663.77685546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 205]}], "orig": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.", "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels."}, {"self_ref": "#/texts/469", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 317.65899658203125, "t": 705.1270141601562, "r": 559.8068237304688, "b": 663.7767944335938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 189]}], "orig": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.", "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement."}, {"self_ref": "#/texts/470", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 472.4300842285156, "r": 131.05624389648438, "b": 462.1210021972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Learning Curve", "text": "Learning Curve", "level": 1}, {"self_ref": "#/texts/471", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 52.78499984741211, "t": 457.22845458984375, "r": 295.558349609375, "b": 262.55181884765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1157]}], "orig": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.", "text": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"self_ref": "#/texts/472", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 249.49008178710938, "r": 164.3289794921875, "b": 239.1809844970703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Impact of Class Labels", "text": "Impact of Class Labels", "level": 1}, {"self_ref": "#/texts/473", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.46699905395508, "t": 234.2884521484375, "r": 295.5567932128906, "b": 83.44783020019531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 910]}], "orig": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of", "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"self_ref": "#/texts/474", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.6860046386719, "t": 460.5964660644531, "r": 559.5849609375, "b": 375.50982666015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 469]}], "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.", "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"self_ref": "#/texts/475", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.9549560546875, "t": 362.6051025390625, "r": 549.860595703125, "b": 352.2960205078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "Impact of Document Split in Train and Test Set", "text": "Impact of Document Split in Train and Test Set", "level": 1}, {"self_ref": "#/texts/476", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.62298583984375, "t": 347.4034729003906, "r": 559.7138061523438, "b": 196.5628204345703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 852]}], "orig": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided.", "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"self_ref": "#/texts/477", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.9549865722656, "t": 183.6580810546875, "r": 418.5477600097656, "b": 173.34898376464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Dataset Comparison", "text": "Dataset Comparison", "level": 1}, {"self_ref": "#/texts/478", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.6860046386719, "t": 168.45645141601562, "r": 559.1881713867188, "b": 83.35986328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 521]}], "orig": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,", "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}, {"self_ref": "#/texts/479", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/480", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.648681640625, "b": 641.85888671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 298]}], "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.", "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"self_ref": "#/texts/481", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 401.0794677734375, "r": 294.047119140625, "b": 348.85986328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 295]}], "orig": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .", "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"self_ref": "#/texts/482", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.46699905395508, "t": 346.28546142578125, "r": 295.55908203125, "b": 206.40382385253906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 793]}], "orig": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts.", "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"self_ref": "#/texts/483", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 186.9390869140625, "r": 156.00534057617188, "b": 176.62998962402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "Example Predictions", "text": "Example Predictions", "level": 1}, {"self_ref": "#/texts/484", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.52899932861328, "t": 171.7364501953125, "r": 295.5584411621094, "b": 86.64982604980469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 481]}], "orig": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence.", "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"self_ref": "#/texts/485", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 317.95501708984375, "t": 706.14013671875, "r": 405.7296142578125, "b": 695.8309936523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "6 CONCLUSION", "text": "6 CONCLUSION", "level": 1}, {"self_ref": "#/texts/486", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.9549865722656, "t": 690.9384765625, "r": 559.7137451171875, "b": 605.850830078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 507]}], "orig": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect.", "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"self_ref": "#/texts/487", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.6860046386719, "t": 603.2664794921875, "r": 559.717041015625, "b": 507.2208251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust.", "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"self_ref": "#/texts/488", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.62298583984375, "t": 504.636474609375, "r": 558.4346923828125, "b": 474.3438415527344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 188]}], "orig": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap.", "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"self_ref": "#/texts/489", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 317.9549865722656, "t": 456.9081115722656, "r": 387.3695983886719, "b": 446.5990295410156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "REFERENCES", "text": "REFERENCES", "level": 1}, {"self_ref": "#/texts/490", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 321.197998046875, "t": 443.29766845703125, "r": 558.2009887695312, "b": 420.8371276855469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 191]}], "orig": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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484.2794494628906, "r": 144.6671600341797, "b": 475.9048156738281, "coord_origin": "BOTTOMLEFT"}, "row_span": 5, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DocLayNet (DLN)", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 154.62899780273438, "t": 484.2794494628906, "r": 174.43577575683594, "b": 475.9048156738281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Table", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 208.44700622558594, "t": 484.2794494628906, "r": 216.78575134277344, "b": 475.9048156738281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "87", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 232.11830139160156, "t": 484.2794494628906, "r": 240.45704650878906, "b": 475.9048156738281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "43", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 256.4979248046875, "t": 484.2794494628906, "r": 264.836669921875, "b": 475.9048156738281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "82", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 78.53099822998047, "t": 484.2794494628906, "r": 144.6671600341797, "b": 475.9048156738281, "coord_origin": "BOTTOMLEFT"}, "row_span": 5, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DocLayNet (DLN)", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 154.62899780273438, "t": 473.3204650878906, "r": 170.5891876220703, "b": 464.9458312988281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Text", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 208.44700622558594, "t": 473.3204650878906, "r": 216.78575134277344, "b": 464.9458312988281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "77", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 234.77235412597656, "t": 473.3204650878906, "r": 237.80299377441406, "b": 464.9458312988281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "-", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 256.4979248046875, "t": 473.3204650878906, "r": 264.836669921875, "b": 464.9458312988281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "84", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 78.53099822998047, "t": 484.2794494628906, "r": 144.6671600341797, "b": 475.9048156738281, "coord_origin": "BOTTOMLEFT"}, "row_span": 5, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DocLayNet (DLN)", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 154.62899780273438, "t": 462.3614501953125, "r": 171.27960205078125, "b": 453.98681640625, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "total", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 208.44700622558594, "t": 462.3614501953125, "r": 216.78575134277344, "b": 453.98681640625, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "59", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 232.11830139160156, "t": 462.3614501953125, "r": 240.45704650878906, "b": 453.98681640625, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "47", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 256.4979248046875, "t": 462.3614501953125, "r": 264.836669921875, "b": 453.98681640625, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "78", "column_header": false, "row_header": false, "row_section": false}]]}}], "key_value_items": [], "pages": {"1": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 1}, "2": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 2}, "3": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 3}, "4": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 4}, "5": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 5}, "6": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 6}, "7": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 7}, "8": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 8}, "9": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 9}}}
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/2206.01062.md b/tests/data/groundtruth/docling_v2/2206.01062.md
index 8523a94a..c5452c57 100644
--- a/tests/data/groundtruth/docling_v2/2206.01062.md
+++ b/tests/data/groundtruth/docling_v2/2206.01062.md
@@ -97,21 +97,21 @@ The annotation campaign was carried out in four phases. In phase one, we identif
 
 Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.
 
-|                |         | % of Total   | % of Total   | % of Total   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   |
-|----------------|---------|--------------|--------------|--------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|
-| class label    | Count   | Train        | Test         | Val          | All                                         | Fin                                         | Man                                         | Sci                                         | Law                                         | Pat                                         | Ten                                         |
-| Caption        | 22524   | 2.04         | 1.77         | 2.32         | 84-89                                       | 40-61                                       | 86-92                                       | 94-99                                       | 95-99                                       | 69-78                                       | n/a                                         |
-| Footnote       | 6318    | 0.60         | 0.31         | 0.58         | 83-91                                       | n/a                                         | 100                                         | 62-88                                       | 85-94                                       | n/a                                         | 82-97                                       |
-| Formula        | 25027   | 2.25         | 1.90         | 2.96         | 83-85                                       | n/a                                         | n/a                                         | 84-87                                       | 86-96                                       | n/a                                         | n/a                                         |
-| List-item      | 185660  | 17.19        | 13.34        | 15.82        | 87-88                                       | 74-83                                       | 90-92                                       | 97-97                                       | 81-85                                       | 75-88                                       | 93-95                                       |
-| Page-footer    | 70878   | 6.51         | 5.58         | 6.00         | 93-94                                       | 88-90                                       | 95-96                                       | 100                                         | 92-97                                       | 100                                         | 96-98                                       |
-| Page-header    | 58022   | 5.10         | 6.70         | 5.06         | 85-89                                       | 66-76                                       | 90-94                                       | 98-100                                      | 91-92                                       | 97-99                                       | 81-86                                       |
-| Picture        | 45976   | 4.21         | 2.78         | 5.31         | 69-71                                       | 56-59                                       | 82-86                                       | 69-82                                       | 80-95                                       | 66-71                                       | 59-76                                       |
-| Section-header | 142884  | 12.60        | 15.77        | 12.85        | 83-84                                       | 76-81                                       | 90-92                                       | 94-95                                       | 87-94                                       | 69-73                                       | 78-86                                       |
-| Table          | 34733   | 3.20         | 2.27         | 3.60         | 77-81                                       | 75-80                                       | 83-86                                       | 98-99                                       | 58-80                                       | 79-84                                       | 70-85                                       |
-| Text           | 510377  | 45.82        | 49.28        | 45.00        | 84-86                                       | 81-86                                       | 88-93                                       | 89-93                                       | 87-92                                       | 71-79                                       | 87-95                                       |
-| Title          | 5071    | 0.47         | 0.30         | 0.50         | 60-72                                       | 24-63                                       | 50-63                                       | 94-100                                      | 82-96                                       | 68-79                                       | 24-56                                       |
-| Total          | 1107470 | 941123       | 99816        | 66531        | 82-83                                       | 71-74                                       | 79-81                                       | 89-94                                       | 86-91                                       | 71-76                                       | 68-85                                       |
+|                |         | % of Total   | % of Total   | % of Total   | % of Total   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   | triple inter-annotator mAP @ 0.5-0.95 (%)   |
+|----------------|---------|--------------|--------------|--------------|--------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|
+| class label    | Count   | Train        | Test         | Val          | All          | Fin                                         | Man                                         | Sci                                         | Law                                         | Pat                                         | Ten                                         |
+| Caption        | 22524   | 2.04         | 1.77         | 2.32         | 84-89        | 40-61                                       | 86-92                                       | 94-99                                       | 95-99                                       | 69-78                                       | n/a                                         |
+| Footnote       | 6318    | 0.60         | 0.31         | 0.58         | 83-91        | n/a                                         | 100                                         | 62-88                                       | 85-94                                       | n/a                                         | 82-97                                       |
+| Formula        | 25027   | 2.25         | 1.90         | 2.96         | 83-85        | n/a                                         | n/a                                         | 84-87                                       | 86-96                                       | n/a                                         | n/a                                         |
+| List-item      | 185660  | 17.19        | 13.34        | 15.82        | 87-88        | 74-83                                       | 90-92                                       | 97-97                                       | 81-85                                       | 75-88                                       | 93-95                                       |
+| Page-footer    | 70878   | 6.51         | 5.58         | 6.00         | 93-94        | 88-90                                       | 95-96                                       | 100                                         | 92-97                                       | 100                                         | 96-98                                       |
+| Page-header    | 58022   | 5.10         | 6.70         | 5.06         | 85-89        | 66-76                                       | 90-94                                       | 98-100                                      | 91-92                                       | 97-99                                       | 81-86                                       |
+| Picture        | 45976   | 4.21         | 2.78         | 5.31         | 69-71        | 56-59                                       | 82-86                                       | 69-82                                       | 80-95                                       | 66-71                                       | 59-76                                       |
+| Section-header | 142884  | 12.60        | 15.77        | 12.85        | 83-84        | 76-81                                       | 90-92                                       | 94-95                                       | 87-94                                       | 69-73                                       | 78-86                                       |
+| Table          | 34733   | 3.20         | 2.27         | 3.60         | 77-81        | 75-80                                       | 83-86                                       | 98-99                                       | 58-80                                       | 79-84                                       | 70-85                                       |
+| Text           | 510377  | 45.82        | 49.28        | 45.00        | 84-86        | 81-86                                       | 88-93                                       | 89-93                                       | 87-92                                       | 71-79                                       | 87-95                                       |
+| Title          | 5071    | 0.47         | 0.30         | 0.50         | 60-72        | 24-63                                       | 50-63                                       | 94-100                                      | 82-96                                       | 68-79                                       | 24-56                                       |
+| Total          | 1107470 | 941123       | 99816        | 66531        | 82-83        | 71-74                                       | 79-81                                       | 89-94                                       | 86-91                                       | 71-76                                       | 68-85                                       |
 
 Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.
 
@@ -209,6 +209,14 @@ Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wis
 | Title          |   77 | Sec.-h. | Sec.-h. | Sec.-h. |
 | Overall        |   72 | 73      | 78      | 77      |
 
+## Learning Curve
+
+One of the fundamental questions related to any dataset is if it is "large enough". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.
+
+## Impact of Class Labels
+
+The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption → Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of
+
 | Class-count    | 11   | 11   | 5   | 5    |
 |----------------|------|------|-----|------|
 | Split          | Doc  | Page | Doc | Page |
@@ -225,14 +233,6 @@ Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wis
 | Title          | 77   | 81   |     |      |
 | All            | 72   | 84   | 78  | 87   |
 
-## Learning Curve
-
-One of the fundamental questions related to any dataset is if it is "large enough". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.
-
-## Impact of Class Labels
-
-The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption → Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of
-
 lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.
 
 ## Impact of Document Split in Train and Test Set
@@ -248,19 +248,19 @@ Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network acros
 |                 |            | Testing on   | Testing on   | Testing on   |
 |-----------------|------------|--------------|--------------|--------------|
 | Training on     | labels     | PLN          | DB           | DLN          |
-|                 | Figure     | 96           | 43           | 23           |
-|                 | Sec-header | 87           | -            | 32           |
+| PubLayNet (PLN) | Figure     | 96           | 43           | 23           |
+| PubLayNet (PLN) | Sec-header | 87           | -            | 32           |
 | PubLayNet (PLN) | Table      | 95           | 24           | 49           |
-|                 | Text       | 96           | -            | 42           |
-|                 | total      | 93           | 34           | 30           |
-|                 | Figure     | 77           | 71           | 31           |
+| PubLayNet (PLN) | Text       | 96           | -            | 42           |
+| PubLayNet (PLN) | total      | 93           | 34           | 30           |
+| DocBank (DB)    | Figure     | 77           | 71           | 31           |
 | DocBank (DB)    | Table      | 19           | 65           | 22           |
-|                 | total      | 48           | 68           | 27           |
-|                 | Figure     | 67           | 51           | 72           |
-|                 | Sec-header | 53           | -            | 68           |
+| DocBank (DB)    | total      | 48           | 68           | 27           |
+| DocLayNet (DLN) | Figure     | 67           | 51           | 72           |
+| DocLayNet (DLN) | Sec-header | 53           | -            | 68           |
 | DocLayNet (DLN) | Table      | 87           | 43           | 82           |
-|                 | Text       | 77           | -            | 84           |
-|                 | total      | 59           | 47           | 78           |
+| DocLayNet (DLN) | Text       | 77           | -            | 84           |
+| DocLayNet (DLN) | total      | 59           | 47           | 78           |
 
 Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .
 
diff --git a/tests/data/groundtruth/docling_v2/2206.01062.pages.json b/tests/data/groundtruth/docling_v2/2206.01062.pages.json
index d6baadab..c9a9314e 100644
--- a/tests/data/groundtruth/docling_v2/2206.01062.pages.json
+++ b/tests/data/groundtruth/docling_v2/2206.01062.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for", "bbox": {"l": 107.29999999999998, "t": 83.69470000000013, "r": 505.06195, "b": 99.67058999999995, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Document-Layout Analysis", "bbox": {"l": 200.117, "t": 103.6196900000001, "r": 411.88367, "b": 119.59558000000015, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Birgit Pfitzmann", "bbox": {"l": 102.06001, "t": 133.67236000000003, "r": 182.63805, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "IBM Research", "bbox": {"l": 114.29401000000001, "t": 147.02423, "r": 170.40337, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Rueschlikon, Switzerland", "bbox": {"l": 90.96701, "t": 158.97924999999998, "r": 193.73123, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "bpf@zurich.ibm.com", "bbox": {"l": 100.02301, "t": 170.93524000000002, "r": 184.67522, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Christoph Auer", "bbox": {"l": 268.62402, "t": 133.67236000000003, "r": 344.59933, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "IBM Research", "bbox": {"l": 278.44302, "t": 147.02423, "r": 334.55237, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Rueschlikon, Switzerland", "bbox": {"l": 255.11602999999997, "t": 158.97924999999998, "r": 357.88025, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "cau@zurich.ibm.com", "bbox": {"l": 263.70404, "t": 170.93524000000002, "r": 349.29272, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Michele Dolfi", "bbox": {"l": 437.6930500000001, "t": 133.67236000000003, "r": 503.60208, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "IBM Research", "bbox": {"l": 442.59305000000006, "t": 147.02423, "r": 498.7023899999999, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Rueschlikon, Switzerland", "bbox": {"l": 419.26505, "t": 158.97924999999998, "r": 522.0293, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "dol@zurich.ibm.com", "bbox": {"l": 428.56104000000005, "t": 170.93524000000002, "r": 512.73505, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Ahmed S. Nassar", "bbox": {"l": 182.26804, "t": 192.05737, "r": 265.39255, "b": 203.22357, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "IBM Research", "bbox": {"l": 195.87103, "t": 205.40923999999995, "r": 251.98038999999997, "b": 214.71429, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Rueschlikon, Switzerland", "bbox": {"l": 172.54303, "t": 217.36425999999994, "r": 275.30725, "b": 226.66931, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "ahn@zurich.ibm.com", "bbox": {"l": 180.52803, "t": 229.32025, "r": 267.3222, "b": 238.62531, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Peter Staar", "bbox": {"l": 361.52802, "t": 192.05737, "r": 414.84821, "b": 203.22357, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "IBM Research", "bbox": {"l": 360.02002, "t": 205.40923999999995, "r": 416.12939, "b": 214.71429, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Rueschlikon, Switzerland", "bbox": {"l": 336.69302, "t": 217.36425999999994, "r": 439.45727999999997, "b": 226.66931, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "taa@zurich.ibm.com", "bbox": {"l": 346.20703, "t": 229.32025, "r": 429.94269, "b": 238.62531, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "ABSTRACT", "bbox": {"l": 53.798035, "t": 247.70288000000005, "r": 111.94354, "b": 258.01202, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Accurate document layout analysis is a key requirement for high-", "bbox": {"l": 53.484001, "t": 262.90454, "r": 295.55591, "b": 271.27917, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "quality PDF document conversion. With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "13", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "USING THE VERTICAL TUBE -", "bbox": {"l": 327.83005, "t": 331.57268999999997, "r": 351.16092, "b": 333.31171, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "MODELS AY11230/11234", "bbox": {"l": 327.83005, "t": 333.18292, "r": 348.30536, "b": 334.92194, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}, {"id": 409, "text": "Figure 1: Four examples of complex page layouts across dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}, {"id": 411, "text": "KEYWORDS", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}}, {"id": 412, "text": "PDF document conversion, layout segmentation, object-detection,", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 616.04218, "coord_origin": "TOPLEFT"}}, {"id": 413, "text": "data set, Machine Learning", "bbox": {"l": 317.95499, "t": 618.62656, "r": 416.94403, "b": 627.00117, "coord_origin": "TOPLEFT"}}, {"id": 414, "text": "ACM Reference Format:", "bbox": {"l": 317.65997, "t": 640.05434, "r": 404.65366, "b": 647.58609, "coord_origin": "TOPLEFT"}}, {"id": 415, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. 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The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 239, "label": "text", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 240, "label": "text", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 241, "label": "text", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 0, "label": "text", "bbox": {"l": 53.466999, "t": 262.90454, "r": 295.56018, "b": 534.29318, "coord_origin": "TOPLEFT"}, "confidence": 0.9823055863380432, "cells": [{"id": 23, "text": "Accurate document layout analysis is a key requirement for high-", "bbox": {"l": 53.484001, "t": 262.90454, "r": 295.55591, "b": 271.27917, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "quality PDF document conversion. With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. 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Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 241, "label": "text", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.466999, "t": 262.90454, "r": 295.56018, "b": 534.29318, "coord_origin": "TOPLEFT"}, "confidence": 0.9823055863380432, "cells": [{"id": 23, "text": "Accurate document layout analysis is a key requirement for high-", "bbox": {"l": 53.484001, "t": 262.90454, "r": 295.55591, "b": 271.27917, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "quality PDF document conversion. With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "caption", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 559.51524, "coord_origin": "TOPLEFT"}, "confidence": 0.7793391346931458, "cells": [{"id": 409, "text": "Figure 1: Four examples of complex page layouts across dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Four examples of complex page layouts across different document categories"}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458844423294067, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; 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Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 241, "label": "text", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.466999, "t": 262.90454, "r": 295.56018, "b": 534.29318, "coord_origin": "TOPLEFT"}, "confidence": 0.9823055863380432, "cells": [{"id": 23, "text": "Accurate document layout analysis is a key requirement for high-", "bbox": {"l": 53.484001, "t": 262.90454, "r": 295.55591, "b": 271.27917, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "quality PDF document conversion. With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "caption", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 559.51524, "coord_origin": "TOPLEFT"}, "confidence": 0.7793391346931458, "cells": [{"id": 409, "text": "Figure 1: Four examples of complex page layouts across dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Four examples of complex page layouts across different document categories"}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458844423294067, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; 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\u2022 Applied computing \u2192 Document analysis ; \u2022 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9208475351333618, "cells": [{"id": 411, "text": "KEYWORDS", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KEYWORDS"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 627.00117, "coord_origin": "TOPLEFT"}, "confidence": 0.9509093761444092, "cells": [{"id": 412, "text": "PDF document conversion, layout segmentation, object-detection,", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 616.04218, "coord_origin": "TOPLEFT"}}, {"id": 413, "text": "data set, Machine Learning", "bbox": {"l": 317.95499, "t": 618.62656, "r": 416.94403, "b": 627.00117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning"}, {"label": "text", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "text", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 295.11798, "b": 672.79189, "coord_origin": "TOPLEFT"}, "confidence": 0.7107337117195129, "cells": [{"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. 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For all other uses, contact the owner/author(s)."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.65997, "t": 640.05434, "r": 404.65366, "b": 647.58609, "coord_origin": "TOPLEFT"}, "confidence": 0.8721982836723328, "cells": [{"id": 414, "text": "ACM Reference Format:", "bbox": {"l": 317.65997, "t": 640.05434, "r": 404.65366, "b": 647.58609, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACM Reference Format:"}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 650.11996, "r": 559.5495, "b": 707.377029, "coord_origin": "TOPLEFT"}, "confidence": 0.9455163478851318, "cells": [{"id": 415, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter", "bbox": {"l": 317.95499, "t": 650.11996, "r": 558.35266, "b": 657.56404, "coord_origin": "TOPLEFT"}}, {"id": 416, "text": "Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for Document-", "bbox": {"l": 317.95499, "t": 660.08296, "r": 559.5495, "b": 667.52703, "coord_origin": "TOPLEFT"}}, {"id": 417, "text": "Layout Analysis. In", "bbox": {"l": 317.95499, "t": 670.04497, "r": 383.30807, "b": 677.48904, "coord_origin": "TOPLEFT"}}, {"id": 418, "text": "Proceedings of the 28th ACM SIGKDD Conference on", "bbox": {"l": 385.798, "t": 670.08482, "r": 558.20032, "b": 677.49701, "coord_origin": "TOPLEFT"}}, {"id": 419, "text": "Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Wash-", "bbox": {"l": 317.95499, "t": 680.04781, "r": 559.00092, "b": 687.46001, "coord_origin": "TOPLEFT"}}, {"id": 420, "text": "ington, DC, USA.", "bbox": {"l": 317.95499, "t": 690.01081, "r": 370.11481, "b": 697.423004, "coord_origin": "TOPLEFT"}}, {"id": 421, "text": "ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/", "bbox": {"l": 371.82999, "t": 689.97096, "r": 558.71655, "b": 697.415031, "coord_origin": "TOPLEFT"}}, {"id": 422, "text": "3534678.3539043", "bbox": {"l": 317.95499, "t": 699.932953, "r": 371.59375, "b": 707.377029, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043"}, {"label": "text", "id": 18, "page_no": 0, "cluster": {"id": 18, "label": "text", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 19, "page_no": 0, "cluster": {"id": 19, "label": "text", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u00a9 2022 Copyright held by the owner/author(s)."}, {"label": "text", "id": 20, "page_no": 0, "cluster": {"id": 20, "label": "text", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACM ISBN 978-1-4503-9385-0/22/08."}, {"label": "text", "id": 21, "page_no": 0, "cluster": {"id": 21, "label": "text", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "https://doi.org/10.1145/3534678.3539043"}], "headers": [{"label": "page_header", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 18.34021, "t": 218.35999000000004, "r": 36.339794, "b": 555.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8537868857383728, "cells": [{"id": 423, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022", "bbox": {"l": 18.34021, "t": 218.35999000000004, "r": 36.339794, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680323123931885, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367701411247253, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996682524681091, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279747962952, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813894629478455, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844210147857666, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792873859405518, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347410202026367, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795214533805847, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330971837043762, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019714355469, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594184160232544, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615143537521362, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447574615478516, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749842882156372, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586857914924622, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680323123931885, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367701411247253, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996682524681091, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279747962952, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813894629478455, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844210147857666, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792873859405518, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347410202026367, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795214533805847, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330971837043762, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019714355469, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594184160232544, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615143537521362, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447574615478516, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749842882156372, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586857914924622, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}], "body": [{"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367701411247253, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996682524681091, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279747962952, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813894629478455, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844210147857666, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792873859405518, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347410202026367, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795214533805847, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330971837043762, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019714355469, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594184160232544, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615143537521362, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447574615478516, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749842882156372, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586857914924622, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}], "headers": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680323123931885, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920252323150635, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.91046142578125, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "picture", "bbox": {"l": 88.32998657226562, "t": 92.88650512695312, "r": 263.70513916015625, "b": 220.56797790527344, "coord_origin": "TOPLEFT"}, "confidence": 0.9698249101638794, "cells": [], "children": [{"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425569772720337, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048321843147278, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920252323150635, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.91046142578125, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.32998657226562, "t": 92.88650512695312, "r": 263.70513916015625, "b": 220.56797790527344, "coord_origin": "TOPLEFT"}, "confidence": 0.9698249101638794, "cells": [], "children": [{"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, 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"TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "caption", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425569772720337, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \u201cinvisible\u201d tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \u201cinvisible\u201d list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \u201ctext in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048321843147278, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}], "body": [{"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.32998657226562, "t": 92.88650512695312, "r": 263.70513916015625, "b": 220.56797790527344, "coord_origin": "TOPLEFT"}, "confidence": 0.9698249101638794, "cells": [], "children": [{"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "caption", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425569772720337, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \u201cinvisible\u201d tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \u201cinvisible\u201d list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \u201ctext in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048321843147278, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}], "headers": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920252323150635, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.91046142578125, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, 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"coord_origin": "TOPLEFT"}}, {"id": 162, "text": "68-85", "bbox": {"l": 487.47034, "t": 283.48654, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625211477279663, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906538486480713, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 104.825, "t": 140.22351000000003, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 418.16003, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "95-99", "bbox": {"l": 428.1217, "t": 162.53954999999996, "r": 447.82986, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "69-78", "bbox": {"l": 457.80051, "t": 162.53954999999996, "r": 477.50867, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "n/a", "bbox": {"l": 495.32489, "t": 162.53954999999996, "r": 507.17846999999995, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Footnote", "bbox": {"l": 104.825, "t": 173.49854000000005, "r": 137.3282, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "6318", "bbox": {"l": 182.035, "t": 173.49854000000005, "r": 198.71251, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "0.60", "bbox": {"l": 219.211, "t": 173.49854000000005, "r": 233.69174000000004, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "0.31", "bbox": {"l": 250.01956, "t": 173.49854000000005, "r": 264.50031, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "0.58", "bbox": {"l": 280.82812, "t": 173.49854000000005, "r": 295.30887, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "83-91", "bbox": {"l": 305.27301, "t": 173.49854000000005, "r": 324.98117, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "n/a", "bbox": {"l": 342.79739, "t": 173.49854000000005, "r": 354.65097, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "100", "bbox": {"l": 371.81265, "t": 173.49854000000005, "r": 384.32077, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "62-88", "bbox": {"l": 398.45181, "t": 173.49854000000005, "r": 418.15997, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "85-94", "bbox": {"l": 428.12164, "t": 173.49854000000005, "r": 447.8298, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "n/a", "bbox": {"l": 465.655, "t": 173.49854000000005, "r": 477.50857999999994, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "82-97", "bbox": {"l": 487.47025, "t": 173.49854000000005, "r": 507.17841, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Formula", "bbox": {"l": 104.825, "t": 184.45752000000005, "r": 135.33766, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "25027", "bbox": {"l": 177.866, "t": 184.45752000000005, "r": 198.71288, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "2.25", "bbox": {"l": 219.211, "t": 184.45752000000005, "r": 233.69174000000004, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1.90", "bbox": {"l": 250.01956, "t": 184.45752000000005, "r": 264.50031, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "2.96", "bbox": {"l": 280.82812, "t": 184.45752000000005, "r": 295.30887, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "83-85", "bbox": {"l": 305.27301, "t": 184.45752000000005, "r": 324.98117, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "n/a", "bbox": {"l": 342.79739, "t": 184.45752000000005, "r": 354.65097, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "n/a", "bbox": {"l": 372.46719, "t": 184.45752000000005, "r": 384.32077, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "84-87", "bbox": {"l": 398.45181, "t": 184.45752000000005, "r": 418.15997, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "86-96", "bbox": {"l": 428.12164, "t": 184.45752000000005, "r": 447.8298, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "n/a", "bbox": {"l": 465.655, "t": 184.45752000000005, "r": 477.50857999999994, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "n/a", "bbox": {"l": 495.3248, "t": 184.45752000000005, "r": 507.17838000000006, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "List-item", "bbox": {"l": 104.825, 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Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847549200057983, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64874, "b": 606.31924, "coord_origin": "TOPLEFT"}, "confidence": 0.8104852437973022, "cells": [{"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.98039311170578, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.974835991859436, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"1": {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 104.825, "t": 140.22351000000003, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 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"assembled": {"elements": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625211477279663, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906538486480713, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 104.825, "t": 140.22351000000003, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 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4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818442463874817, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847549200057983, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64874, "b": 606.31924, "coord_origin": "TOPLEFT"}, "confidence": 0.8104852437973022, "cells": [{"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.98039311170578, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.974835991859436, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{3}$https://arxiv.org/"}], "body": [{"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906538486480713, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 104.825, "t": 140.22351000000003, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 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"coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 10, "end_col_offset_idx": 11, "text": "71-76", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 487.47034, "t": 283.48654, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 11, "end_col_offset_idx": 12, "text": "68-85", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "picture", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 53.05910873413086, "t": 310.7912902832031, "r": 295.8505554199219, "b": 540.8641357421875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847874045372009, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818442463874817, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847549200057983, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64874, "b": 606.31924, "coord_origin": "TOPLEFT"}, "confidence": 0.8104852437973022, "cells": [{"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.98039311170578, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.974835991859436, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{3}$https://arxiv.org/"}], "headers": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625211477279663, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.887782871723175, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534672856330872, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264117002487183, "cells": [], "children": [{"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, 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"the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691525101661682, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873710632324219, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544305205345154, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611717462539673, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.982954204082489, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.887782871723175, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534672856330872, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264117002487183, "cells": [], "children": [{"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 84, "text": 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326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.902275800704956, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691525101661682, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "text", "id": 22, "page_no": 4, "cluster": {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"label": "caption", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873710632324219, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544305205345154, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611717462539673, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.982954204082489, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other\u2019s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}], "body": [{"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264117002487183, "cells": [], "children": [{"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.902275800704956, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691525101661682, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "text", "id": 22, "page_no": 4, "cluster": {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"label": "caption", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873710632324219, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544305205345154, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611717462539673, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.982954204082489, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other\u2019s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}], "headers": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.887782871723175, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534672856330872, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "human", "bbox": {"l": 132.36501, "t": 197.97351000000003, "r": 157.99098, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "MRCNN", "bbox": {"l": 173.505, "t": 197.97351000000003, "r": 204.61841, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "FRCNN", "bbox": {"l": 220.13028, "t": 197.97351000000003, "r": 248.06958, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "YOLO", "bbox": {"l": 258.03125, "t": 197.97351000000003, "r": 280.17825, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "R50", "bbox": {"l": 168.39301, "t": 208.93255999999997, "r": 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{"id": 68, "text": "74.6", "bbox": {"l": 261.86804, "t": 297.00253, "r": 276.34879, "b": 305.37717, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Table", "bbox": {"l": 67.663002, "t": 307.96155, "r": 87.46978, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "77-81", "bbox": {"l": 135.32401, "t": 307.96155, "r": 155.03215, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "82.2", "bbox": {"l": 167.95399, "t": 307.96155, "r": 182.43472, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "82.9", "bbox": {"l": 194.0462, "t": 307.96155, "r": 208.52695, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "82.2", "bbox": {"l": 226.86324000000002, "t": 307.96155, "r": 241.34396, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "86.3", "bbox": {"l": 261.86804, "t": 307.96155, "r": 276.34879, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Text", "bbox": {"l": 67.663002, "t": 318.91953, "r": 83.623199, "b": 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167.95399, "t": 329.87854, "r": 182.43472, "b": 338.25317, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "80.4", "bbox": {"l": 194.0462, "t": 329.87854, "r": 208.52695, "b": 338.25317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "79.9", "bbox": {"l": 226.86324000000002, "t": 329.87854, "r": 241.34396, "b": 338.25317, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "82.7", "bbox": {"l": 261.86804, "t": 329.87854, "r": 276.34879, "b": 338.25317, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "All", "bbox": {"l": 67.663002, "t": 341.23654, "r": 78.628906, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "82-83", "bbox": {"l": 135.32401, "t": 341.23654, "r": 155.03215, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "72.4", "bbox": {"l": 167.95399, "t": 341.23654, "r": 182.43472, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "73.5", "bbox": {"l": 194.0462, "t": 341.23654, "r": 208.52695, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "73.4", "bbox": {"l": 226.86324000000002, "t": 341.23654, "r": 241.34396, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "76.8", "bbox": {"l": 261.86804, "t": 341.23654, "r": 276.34879, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "0", "bbox": {"l": 349.16577, "t": 246.68017999999995, "r": 352.48175, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "20", "bbox": {"l": 385.93698, "t": 246.68017999999995, "r": 392.56894, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "40", "bbox": {"l": 424.366, "t": 246.68017999999995, "r": 430.99796, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "60", "bbox": {"l": 462.79504000000003, "t": 246.68017999999995, "r": 469.427, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "80", "bbox": {"l": 501.22406, "t": 246.68017999999995, "r": 507.85602, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "100", "bbox": {"l": 537.99524, "t": 246.68017999999995, "r": 547.94318, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "% of DocLayNet training set", "bbox": {"l": 410.28143, "t": 253.80840999999998, "r": 483.47278000000006, "b": 259.88251, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "50", "bbox": {"l": 330.93539, "t": 218.38464, "r": 337.56735, "b": 224.45874000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "55", "bbox": {"l": 330.93539, "t": 192.08660999999995, "r": 337.56735, "b": 198.16071, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "60", "bbox": {"l": 330.93539, "t": 165.78864, "r": 337.56735, "b": 171.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "65", "bbox": {"l": 330.93539, "t": 139.49059999999997, "r": 337.56735, "b": 145.56470000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "70", "bbox": {"l": 330.93539, "t": 113.19263000000001, "r": 337.56735, "b": 119.26671999999996, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "mAP 0.50:0.95", "bbox": {"l": 322.92276, "t": 148.37689, "r": 328.99686, "b": 186.79218000000003, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "10", "bbox": {"l": 470.97235, "t": 235.36676, "r": 477.6055, "b": 241.44086000000004, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "1", "bbox": {"l": 477.65662, "t": 234.82390999999996, "r": 479.97778000000005, "b": 239.07581000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "10", "bbox": {"l": 531.55127, "t": 235.41234999999995, "r": 538.18445, "b": 241.48645, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "2", "bbox": {"l": 538.23553, "t": 234.86951, "r": 540.5567, "b": 239.1214, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "50", "bbox": {"l": 404.91125, "t": 216.00005999999996, "r": 411.54321, "b": 222.07416, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "55", "bbox": {"l": 404.91125, "t": 200.22125000000005, "r": 411.54321, "b": 206.29534999999998, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "60", "bbox": {"l": 404.91125, "t": 184.44244000000003, "r": 411.54321, "b": 190.51653999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "65", "bbox": {"l": 404.91125, "t": 168.66364, "r": 411.54321, "b": 174.73773000000006, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "70", "bbox": {"l": 404.91125, "t": 152.88489000000004, "r": 411.54321, "b": 158.95898, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. 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The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.528999, "t": 370.92755, "r": 295.55612, "b": 576.56317, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579004049301147, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.9876052141189575, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "table", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "table", "bbox": {"l": 67.663002, "t": 197.97351000000003, "r": 280.17825, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}, "confidence": 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"end_col_offset_idx": 6, "text": "76.8", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 317.95499, "t": 279.01599, "r": 559.80579, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.963992178440094, "cells": [{"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.528999, "t": 370.92755, "r": 295.55612, "b": 576.56317, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579004049301147, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.9876052141189575, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}], "body": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64874, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9782734513282776, "cells": [{"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. 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"end_col_offset_idx": 6, "text": "76.8", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 317.95499, "t": 279.01599, "r": 559.80579, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.963992178440094, "cells": [{"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.528999, "t": 370.92755, "r": 295.55612, "b": 576.56317, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579004049301147, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.9876052141189575, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}], "headers": [{"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8662774562835693, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "6", "bbox": {"l": 179.31816, "t": 153.10051999999996, "r": 183.48753, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "5", "bbox": {"l": 213.33669, "t": 153.10051999999996, "r": 217.50606, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "4", "bbox": {"l": 247.35521, "t": 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"b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "72", "bbox": {"l": 151.07401, "t": 285.40454, "r": 159.41275, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "73", "bbox": {"l": 177.23795, "t": 285.40454, "r": 185.57669, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "78", "bbox": {"l": 211.25645, "t": 285.40454, "r": 219.5952, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "77", "bbox": {"l": 245.27496, "t": 285.40454, "r": 253.61371, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Class-count", "bbox": {"l": 358.63901, "t": 153.10051999999996, "r": 401.73154, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "11", "bbox": {"l": 440.22501, "t": 153.10051999999996, "r": 448.56375, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "5", "bbox": {"l": 494.38, "t": 153.10051999999996, "r": 498.54938, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Split", "bbox": {"l": 358.63901, "t": 164.05951000000005, "r": 375.27167, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Doc", "bbox": {"l": 423.341, "t": 164.05951000000005, "r": 438.0459, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Page", 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460.90115000000003, "b": 282.42117, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "84", "bbox": {"l": 478.59399, "t": 274.04657, "r": 486.93274, "b": 282.42117, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "90", "bbox": {"l": 504.6324200000001, "t": 274.04657, "r": 512.97119, "b": 282.42117, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Title", "bbox": {"l": 358.63901, "t": 285.00552, "r": 375.63034, "b": 293.38015999999993, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "77", "bbox": {"l": 426.52399, "t": 285.00552, "r": 434.86273, "b": 293.38015999999993, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "81", "bbox": {"l": 452.56240999999994, "t": 285.00552, "r": 460.90115000000003, "b": 293.38015999999993, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "All", "bbox": {"l": 358.63901, "t": 296.36255, "r": 369.60492, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "72", "bbox": {"l": 426.52399, "t": 296.36255, "r": 434.86273, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "84", "bbox": {"l": 452.56240999999994, "t": 296.36255, "r": 460.90115000000003, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "78", "bbox": {"l": 478.59399, "t": 296.36255, "r": 486.93274, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "87", "bbox": {"l": 504.6324200000001, "t": 296.36255, "r": 512.97119, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9316117763519287, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.8296932578086853, "cells": [{"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.87362140417099, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 261.46832, "b": 293.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9869412779808044, "cells": [{"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", 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"coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}, "confidence": 0.9555687308311462, "cells": [{"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740145802497864, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572024941444397, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589216113090515, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"4": {"label": "table", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "table", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 261.46832, "b": 293.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9869412779808044, "cells": [{"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, 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131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}, "confidence": 0.9555687308311462, "cells": [{"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Learning Curve"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740145802497864, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "text", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is \u201clarge enough\u201d. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572024941444397, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589216113090515, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "body": [{"label": "text", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.8296932578086853, "cells": [{"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels."}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.87362140417099, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement."}, {"label": "table", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "table", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 261.46832, "b": 293.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9869412779808044, "cells": [{"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "6", "bbox": {"l": 179.31816, "t": 153.10051999999996, "r": 183.48753, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "5", "bbox": {"l": 213.33669, "t": 153.10051999999996, "r": 217.50606, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "4", "bbox": {"l": 247.35521, "t": 153.10051999999996, "r": 251.52458, "b": 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131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}, "confidence": 0.9555687308311462, "cells": [{"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Learning Curve"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740145802497864, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "text", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is \u201clarge enough\u201d. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572024941444397, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589216113090515, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "headers": [{"label": "page_header", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9316117763519287, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.32153, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 295.64868, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "cLayNet data-sets. By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Testing on", "bbox": {"l": 217.74099999999999, "t": 175.01855, "r": 256.26065, "b": 183.39319, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Training on", "bbox": {"l": 89.954002, "t": 185.97655999999995, "r": 133.24379, "b": 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{"id": 30, "text": "42", "bbox": {"l": 256.49792, "t": 230.21155, "r": 264.83667, "b": 238.58618, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "total", "bbox": {"l": 154.629, "t": 241.16956000000005, "r": 171.2796, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "93", "bbox": {"l": 208.44701, "t": 241.16956000000005, "r": 216.78575000000004, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "34", "bbox": {"l": 232.1183, "t": 241.16956000000005, "r": 240.45705, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "30", "bbox": {"l": 256.49792, "t": 241.16956000000005, "r": 264.83667, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "DocBank (DB)", "bbox": {"l": 78.530998, "t": 263.48650999999995, "r": 131.19963, "b": 271.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Figure", "bbox": {"l": 154.629, "t": 252.52752999999996, "r": 177.92371, "b": 260.90216, "coord_origin": 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"coord_origin": "TOPLEFT"}}, {"id": 44, "text": "total", "bbox": {"l": 154.629, "t": 274.44556, "r": 171.2796, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "48", "bbox": {"l": 208.44701, "t": 274.44556, "r": 216.78575000000004, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "68", "bbox": {"l": 232.1183, "t": 274.44556, "r": 240.45705, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "27", "bbox": {"l": 256.49792, "t": 274.44556, "r": 264.83667, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "DocLayNet (DLN)", "bbox": {"l": 78.530998, "t": 307.72055, "r": 144.66716, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Figure", "bbox": {"l": 154.629, "t": 285.80255, "r": 177.92371, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "67", "bbox": {"l": 208.44701, "t": 285.80255, "r": 216.78575000000004, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "51", "bbox": {"l": 232.1183, "t": 285.80255, "r": 240.45705, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "72", "bbox": {"l": 256.49792, "t": 285.80255, "r": 264.83667, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Sec-header", "bbox": {"l": 154.629, "t": 296.76154, "r": 194.72675, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "53", "bbox": {"l": 208.44701, "t": 296.76154, "r": 216.78575000000004, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "-", "bbox": {"l": 234.77235, "t": 296.76154, "r": 237.80299000000002, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "68", "bbox": {"l": 256.49792, "t": 296.76154, "r": 264.83667, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Table", "bbox": {"l": 154.629, "t": 307.72055, "r": 174.43578, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "87", "bbox": {"l": 208.44701, "t": 307.72055, "r": 216.78575000000004, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "43", "bbox": {"l": 232.1183, "t": 307.72055, "r": 240.45705, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "82", "bbox": {"l": 256.49792, "t": 307.72055, "r": 264.83667, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "Text", "bbox": {"l": 154.629, "t": 318.67953, "r": 170.58919, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "77", "bbox": {"l": 208.44701, "t": 318.67953, "r": 216.78575000000004, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "-", "bbox": {"l": 234.77235, "t": 318.67953, "r": 237.80299000000002, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "84", "bbox": {"l": 256.49792, "t": 318.67953, "r": 264.83667, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "total", "bbox": {"l": 154.629, "t": 329.63855, "r": 171.2796, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "59", "bbox": {"l": 208.44701, "t": 329.63855, "r": 216.78575000000004, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "47", "bbox": {"l": 232.1183, "t": 329.63855, "r": 240.45705, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "78", "bbox": {"l": 256.49792, "t": 329.63855, "r": 264.83667, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319990873336792, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64868, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9824119210243225, "cells": [{"id": 2, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.32153, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 295.64868, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "cLayNet data-sets. By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. 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In", "bbox": {"l": 333.39099, "t": 388.55236999999994, "r": 367.4339, "b": 395.06592, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "2017 14th IAPR International Conference on Document Analysis and", "bbox": {"l": 369.17401, "t": 388.58725000000004, "r": 558.20422, "b": 395.07288, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Recognition (ICDAR)", "bbox": {"l": 333.39099, "t": 396.55725, "r": 390.87601, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": ", volume 01, pages 1404-1410, 2017.", "bbox": {"l": 390.87698, "t": 396.52237, "r": 492.17831, "b": 403.03592, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 390.92053, "r": 294.04712, "b": 443.14014, "coord_origin": "TOPLEFT"}, "confidence": 0.9839397668838501, "cells": [{"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 321.198, "t": 404.49237, "r": 558.20013, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9257484078407288, "cells": [{"id": 148, "text": "[3]", "bbox": {"l": 321.198, "t": 404.49237, "r": 329.53583, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian", "bbox": {"l": 331.30682, "t": 404.49237, "r": 558.19958, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and", "bbox": {"l": 333.39099, "t": 412.46335, "r": 558.20013, "b": 418.9769, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "Recognition (cTDaR), April 2019. http://sac.founderit.com/.", "bbox": {"l": 333.39099, "t": 420.43335, "r": 501.80127, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "list_item", "bbox": {"l": 321.198, "t": 428.40335, "r": 559.37872, "b": 458.8269, "coord_origin": "TOPLEFT"}, "confidence": 0.9270963668823242, "cells": [{"id": 152, "text": "[4]", "bbox": {"l": 321.198, "t": 428.40335, "r": 329.91299, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on", "bbox": {"l": 331.7641, "t": 428.40335, "r": 558.19885, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "scientific literature parsing. In", "bbox": {"l": 333.39099, "t": 436.3733500000001, "r": 423.77225, "b": 442.8869, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Proceedings of the International Conference on", "bbox": {"l": 425.909, "t": 436.40823, "r": 558.20178, "b": 442.89386, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 444.37823, "r": 429.42697, "b": 450.86386, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": ", ICDAR, pages 605-617. LNCS 12824, Springer-", "bbox": {"l": 429.42400999999995, "t": 444.34335, "r": 559.37872, "b": 450.85689999999994, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Verlag, sep 2021.", "bbox": {"l": 332.819, "t": 452.31335, "r": 380.01764, "b": 458.8269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "list_item", "bbox": {"l": 321.198, "t": 460.28336, "r": 559.02625, "b": 490.70792, "coord_origin": "TOPLEFT"}, "confidence": 0.9289959073066711, "cells": [{"id": 159, "text": "[5]", "bbox": {"l": 321.198, "t": 460.28336, "r": 329.22977, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin,", "bbox": {"l": 330.93576, "t": 460.28336, "r": 558.97156, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis:", "bbox": {"l": 333.39099, "t": 468.25336, "r": 559.02625, "b": 474.76691, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "not dead yet.", "bbox": {"l": 333.39099, "t": 476.22437, "r": 368.85431, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "International Journal on Document Analysis and Recognition (IJDAR)", "bbox": {"l": 370.811, "t": 476.25925, "r": 557.46326, "b": 482.74487, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": ",", "bbox": {"l": 557.46503, "t": 476.22437, "r": 558.96857, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "pages 1-11, 01 2022.", "bbox": {"l": 333.39099, "t": 484.19437, "r": 390.82715, "b": 490.70792, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "list_item", "bbox": {"l": 321.198, "t": 492.16437, "r": 558.20361, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.927467405796051, "cells": [{"id": 166, "text": "[6]", "bbox": {"l": 321.198, "t": 492.16437, "r": 329.42145, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset", "bbox": {"l": 331.16812, "t": 492.16437, "r": 558.20361, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "ever for document layout analysis. In", "bbox": {"l": 333.39099, "t": 500.13437, "r": 438.6101100000001, "b": 506.64792, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "Proceedings of the International Conference", "bbox": {"l": 440.349, "t": 500.16925, "r": 558.19958, "b": 506.65488, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "on Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 508.13925, "r": 441.40118, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": ", ICDAR, pages 1015-1022, sep 2019.", "bbox": {"l": 441.4019799999999, "t": 508.10437, "r": 544.78162, "b": 514.61792, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 321.19797, "t": 516.07437, "r": 558.9715, "b": 554.46889, "coord_origin": "TOPLEFT"}, "confidence": 0.9426887035369873, "cells": [{"id": 172, "text": "[7]", "bbox": {"l": 321.19797, "t": 516.07437, "r": 329.74161, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and", "bbox": {"l": 331.55634, "t": 516.07437, "r": 558.19897, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In", "bbox": {"l": 333.39099, "t": 524.0443700000001, "r": 558.19891, "b": 530.55792, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "Proceedings of the 28th International Conference on Computational Linguistics", "bbox": {"l": 333.39099, "t": 532.04922, "r": 557.40228, "b": 538.53487, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": ",", "bbox": {"l": 557.40399, "t": 532.01437, "r": 558.96893, "b": 538.5278900000001, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "COLING, pages 949-960. International Committee on Computational Linguistics,", "bbox": {"l": 333.39099, "t": 539.98438, "r": 558.9715, "b": 546.49789, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "dec 2020.", "bbox": {"l": 333.39099, "t": 547.95535, "r": 359.31955, "b": 554.46889, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "list_item", "bbox": {"l": 321.198, "t": 555.92535, "r": 558.90222, "b": 578.38586, "coord_origin": "TOPLEFT"}, "confidence": 0.9139776229858398, "cells": [{"id": 179, "text": "[8]", "bbox": {"l": 321.198, "t": 555.92535, "r": 329.7088, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction", "bbox": {"l": 331.51654, "t": 555.92535, "r": 558.19891, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "from pdf sources based on rule-based system using integrated formats. In", "bbox": {"l": 333.39099, "t": 563.89536, "r": 535.54352, "b": 570.40889, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "SemWe-", "bbox": {"l": 536.96399, "t": 563.93024, "r": 558.90222, "b": 570.4158600000001, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "bEval@ESWC", "bbox": {"l": 333.39099, "t": 571.9002399999999, "r": 371.94217, "b": 578.38586, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": ", 2016.", "bbox": {"l": 371.94299, "t": 571.86536, "r": 389.72617, "b": 578.3788900000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "list_item", "bbox": {"l": 321.198, "t": 579.83536, "r": 559.27448, "b": 610.2589, "coord_origin": "TOPLEFT"}, "confidence": 0.9113092422485352, "cells": [{"id": 185, "text": "[9]", "bbox": {"l": 321.198, "t": 579.83536, "r": 329.45999, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature", "bbox": {"l": 331.21487, "t": 579.83536, "r": 558.19843, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "hierarchies for accurate object detection and semantic segmentation. In", "bbox": {"l": 333.39099, "t": 587.8053600000001, "r": 543.05487, "b": 594.31889, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "IEEE", "bbox": {"l": 544.98499, "t": 587.84024, "r": 558.20148, "b": 594.32587, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Conference on Computer Vision and Pattern Recognition", "bbox": {"l": 333.39099, "t": 595.81024, "r": 491.61166, "b": 602.29587, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": ", CVPR, pages 580-587.", "bbox": {"l": 491.61301, "t": 595.77536, "r": 559.27448, "b": 602.28889, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "IEEE Computer Society, jun 2014.", "bbox": {"l": 333.39099, "t": 603.74536, "r": 428.59726000000006, "b": 610.2589, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574695229530334, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "list_item", "bbox": {"l": 317.95499, "t": 611.71536, "r": 558.20203, "b": 626.20686, "coord_origin": "TOPLEFT"}, "confidence": 0.9028682708740234, "cells": [{"id": 192, "text": "[10]", "bbox": {"l": 317.95499, "t": 611.71536, "r": 329.54767, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Ross B. Girshick. Fast R-CNN. In", "bbox": {"l": 331.31268, "t": 611.71536, "r": 425.1554, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "2015 IEEE International Conference on Computer", "bbox": {"l": 426.77802, "t": 611.75024, "r": 558.20203, "b": 618.23587, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Vision", "bbox": {"l": 333.39099, "t": 619.72124, "r": 350.59537, "b": 626.20686, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": ", ICCV, pages 1440-1448. IEEE Computer Society, dec 2015.", "bbox": {"l": 350.59598, "t": 619.68637, "r": 518.58777, "b": 626.19989, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859862327575684, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "list_item", "bbox": {"l": 317.95499, "t": 627.65637, "r": 558.20142, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9127580523490906, "cells": [{"id": 197, "text": "[11]", "bbox": {"l": 317.95499, "t": 627.65637, "r": 329.5459, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 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By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. 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Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. 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"end_col_offset_idx": 4, "text": "47", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 256.49792, "t": 329.63855, "r": 264.83667, "b": 338.01318, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "78", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9800511598587036, "cells": [{"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9676095843315125, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table", "bbox": {"l": 331.53516, "t": 348.70233, "r": 558.19904, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "competition. In", "bbox": {"l": 333.39099, "t": 356.67236, "r": 379.36414, "b": 363.18591, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "2013 12th International Conference on Document Analysis and", "bbox": {"l": 381.37201, "t": 356.70724, "r": 558.20099, "b": 363.19287, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Recognition", "bbox": {"l": 333.39099, "t": 364.67724999999996, "r": 365.59601, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ", pages 1449-1453, 2013.", "bbox": {"l": 365.59601, "t": 364.64236, "r": 434.29489, "b": 371.15591, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. In 2013 12th International Conference on Document Analysis and Recognition , pages 1449-1453, 2013."}, {"label": "list_item", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 321.198, "t": 372.61237, "r": 559.37982, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9480941295623779, "cells": [{"id": 141, "text": "[2]", "bbox": {"l": 321.198, "t": 372.61237, "r": 329.85956, "b": 379.12592, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Ic-", "bbox": {"l": 331.69931, "t": 372.61237, "r": 559.37976, "b": 379.12592, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "dar2017 competition on recognition of documents with complex layouts -", "bbox": {"l": 333.39099, "t": 380.58237, "r": 559.37982, "b": 387.09592, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "rdcl2017. In", "bbox": {"l": 333.39099, "t": 388.55236999999994, "r": 367.4339, "b": 395.06592, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "2017 14th IAPR International Conference on Document Analysis and", "bbox": {"l": 369.17401, "t": 388.58725000000004, "r": 558.20422, "b": 395.07288, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Recognition (ICDAR)", "bbox": {"l": 333.39099, "t": 396.55725, "r": 390.87601, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": ", volume 01, pages 1404-1410, 2017.", "bbox": {"l": 390.87698, "t": 396.52237, "r": 492.17831, "b": 403.03592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts rdcl2017. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) , volume 01, pages 1404-1410, 2017."}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 390.92053, "r": 294.04712, "b": 443.14014, "coord_origin": "TOPLEFT"}, "confidence": 0.9839397668838501, "cells": [{"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet\u2019s other labels as specified in table 3, and also PubLayNet\u2019s List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "list_item", "id": 17, "page_no": 7, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 321.198, "t": 404.49237, "r": 558.20013, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9257484078407288, "cells": [{"id": 148, "text": "[3]", "bbox": {"l": 321.198, "t": 404.49237, "r": 329.53583, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian", "bbox": {"l": 331.30682, "t": 404.49237, "r": 558.19958, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and", "bbox": {"l": 333.39099, "t": 412.46335, "r": 558.20013, "b": 418.9769, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "Recognition (cTDaR), April 2019. http://sac.founderit.com/.", "bbox": {"l": 333.39099, "t": 420.43335, "r": 501.80127, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[3] Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and Recognition (cTDaR), April 2019. http://sac.founderit.com/."}, {"label": "list_item", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "list_item", "bbox": {"l": 321.198, "t": 428.40335, "r": 559.37872, "b": 458.8269, "coord_origin": "TOPLEFT"}, "confidence": 0.9270963668823242, "cells": [{"id": 152, "text": "[4]", "bbox": {"l": 321.198, "t": 428.40335, "r": 329.91299, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on", "bbox": {"l": 331.7641, "t": 428.40335, "r": 558.19885, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "scientific literature parsing. In", "bbox": {"l": 333.39099, "t": 436.3733500000001, "r": 423.77225, "b": 442.8869, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Proceedings of the International Conference on", "bbox": {"l": 425.909, "t": 436.40823, "r": 558.20178, "b": 442.89386, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 444.37823, "r": 429.42697, "b": 450.86386, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": ", ICDAR, pages 605-617. LNCS 12824, Springer-", "bbox": {"l": 429.42400999999995, "t": 444.34335, "r": 559.37872, "b": 450.85689999999994, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Verlag, sep 2021.", "bbox": {"l": 332.819, "t": 452.31335, "r": 380.01764, "b": 458.8269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[4] Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on scientific literature parsing. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 605-617. LNCS 12824, SpringerVerlag, sep 2021."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "list_item", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 321.198, "t": 460.28336, "r": 559.02625, "b": 490.70792, "coord_origin": "TOPLEFT"}, "confidence": 0.9289959073066711, "cells": [{"id": 159, "text": "[5]", "bbox": {"l": 321.198, "t": 460.28336, "r": 329.22977, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin,", "bbox": {"l": 330.93576, "t": 460.28336, "r": 558.97156, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis:", "bbox": {"l": 333.39099, "t": 468.25336, "r": 559.02625, "b": 474.76691, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "not dead yet.", "bbox": {"l": 333.39099, "t": 476.22437, "r": 368.85431, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "International Journal on Document Analysis and Recognition (IJDAR)", "bbox": {"l": 370.811, "t": 476.25925, "r": 557.46326, "b": 482.74487, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": ",", "bbox": {"l": 557.46503, "t": 476.22437, "r": 558.96857, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "pages 1-11, 01 2022.", "bbox": {"l": 333.39099, "t": 484.19437, "r": 390.82715, "b": 490.70792, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[5] Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin, Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis: not dead yet. International Journal on Document Analysis and Recognition (IJDAR) , pages 1-11, 01 2022."}, {"label": "list_item", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 321.198, "t": 492.16437, "r": 558.20361, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.927467405796051, "cells": [{"id": 166, "text": "[6]", "bbox": {"l": 321.198, "t": 492.16437, "r": 329.42145, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset", "bbox": {"l": 331.16812, "t": 492.16437, "r": 558.20361, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "ever for document layout analysis. In", "bbox": {"l": 333.39099, "t": 500.13437, "r": 438.6101100000001, "b": 506.64792, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "Proceedings of the International Conference", "bbox": {"l": 440.349, "t": 500.16925, "r": 558.19958, "b": 506.65488, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "on Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 508.13925, "r": 441.40118, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": ", ICDAR, pages 1015-1022, sep 2019.", "bbox": {"l": 441.4019799999999, "t": 508.10437, "r": 544.78162, "b": 514.61792, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[6] Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset ever for document layout analysis. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 1015-1022, sep 2019."}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 321.19797, "t": 516.07437, "r": 558.9715, "b": 554.46889, "coord_origin": "TOPLEFT"}, "confidence": 0.9426887035369873, "cells": [{"id": 172, "text": "[7]", "bbox": {"l": 321.19797, "t": 516.07437, "r": 329.74161, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and", "bbox": {"l": 331.55634, "t": 516.07437, "r": 558.19897, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In", "bbox": {"l": 333.39099, "t": 524.0443700000001, "r": 558.19891, "b": 530.55792, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "Proceedings of the 28th International Conference on Computational Linguistics", "bbox": {"l": 333.39099, "t": 532.04922, "r": 557.40228, "b": 538.53487, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": ",", "bbox": {"l": 557.40399, "t": 532.01437, "r": 558.96893, "b": 538.5278900000001, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "COLING, pages 949-960. International Committee on Computational Linguistics,", "bbox": {"l": 333.39099, "t": 539.98438, "r": 558.9715, "b": 546.49789, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "dec 2020.", "bbox": {"l": 333.39099, "t": 547.95535, "r": 359.31955, "b": 554.46889, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[7] Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In Proceedings of the 28th International Conference on Computational Linguistics , COLING, pages 949-960. International Committee on Computational Linguistics, dec 2020."}, {"label": "list_item", "id": 19, "page_no": 7, "cluster": {"id": 19, "label": "list_item", "bbox": {"l": 321.198, "t": 555.92535, "r": 558.90222, "b": 578.38586, "coord_origin": "TOPLEFT"}, "confidence": 0.9139776229858398, "cells": [{"id": 179, "text": "[8]", "bbox": {"l": 321.198, "t": 555.92535, "r": 329.7088, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction", "bbox": {"l": 331.51654, "t": 555.92535, "r": 558.19891, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "from pdf sources based on rule-based system using integrated formats. In", "bbox": {"l": 333.39099, "t": 563.89536, "r": 535.54352, "b": 570.40889, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "SemWe-", "bbox": {"l": 536.96399, "t": 563.93024, "r": 558.90222, "b": 570.4158600000001, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "bEval@ESWC", "bbox": {"l": 333.39099, "t": 571.9002399999999, "r": 371.94217, "b": 578.38586, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": ", 2016.", "bbox": {"l": 371.94299, "t": 571.86536, "r": 389.72617, "b": 578.3788900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[8] Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction from pdf sources based on rule-based system using integrated formats. In SemWebEval@ESWC , 2016."}, {"label": "list_item", "id": 21, "page_no": 7, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 321.198, "t": 579.83536, "r": 559.27448, "b": 610.2589, "coord_origin": "TOPLEFT"}, "confidence": 0.9113092422485352, "cells": [{"id": 185, "text": "[9]", "bbox": {"l": 321.198, "t": 579.83536, "r": 329.45999, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature", "bbox": {"l": 331.21487, "t": 579.83536, "r": 558.19843, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "hierarchies for accurate object detection and semantic segmentation. In", "bbox": {"l": 333.39099, "t": 587.8053600000001, "r": 543.05487, "b": 594.31889, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "IEEE", "bbox": {"l": 544.98499, "t": 587.84024, "r": 558.20148, "b": 594.32587, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Conference on Computer Vision and Pattern Recognition", "bbox": {"l": 333.39099, "t": 595.81024, "r": 491.61166, "b": 602.29587, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": ", CVPR, pages 580-587.", "bbox": {"l": 491.61301, "t": 595.77536, "r": 559.27448, "b": 602.28889, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "IEEE Computer Society, jun 2014.", "bbox": {"l": 333.39099, "t": 603.74536, "r": 428.59726000000006, "b": 610.2589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[9] Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. 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Fast R-CNN. In", "bbox": {"l": 331.31268, "t": 611.71536, "r": 425.1554, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "2015 IEEE International Conference on Computer", "bbox": {"l": 426.77802, "t": 611.75024, "r": 558.20203, "b": 618.23587, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Vision", "bbox": {"l": 333.39099, "t": 619.72124, "r": 350.59537, "b": 626.20686, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": ", ICCV, pages 1440-1448. IEEE Computer Society, dec 2015.", "bbox": {"l": 350.59598, "t": 619.68637, "r": 518.58777, "b": 626.19989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[10] Ross B. Girshick. Fast R-CNN. In 2015 IEEE International Conference on Computer Vision , ICCV, pages 1440-1448. IEEE Computer Society, dec 2015."}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859862327575684, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "list_item", "id": 20, "page_no": 7, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 317.95499, "t": 627.65637, "r": 558.20142, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9127580523490906, "cells": [{"id": 197, "text": "[11]", "bbox": {"l": 317.95499, "t": 627.65637, "r": 329.5459, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards", "bbox": {"l": 331.31064, "t": 627.65637, "r": 558.20142, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "real-time object detection with region proposal networks.", "bbox": {"l": 333.39099, "t": 635.62637, "r": 497.50909, "b": 642.13989, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "IEEE Transactions on", "bbox": {"l": 500.01401, "t": 635.66124, "r": 558.19885, "b": 642.14687, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Pattern Analysis and Machine Intelligence", "bbox": {"l": 333.39099, "t": 643.6312399999999, "r": 449.38620000000003, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": ", 39(6):1137-1149, 2017.", "bbox": {"l": 449.38699, "t": 643.59637, "r": 515.74268, "b": 650.10989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[11] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence , 39(6):1137-1149, 2017."}, {"label": "list_item", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 317.95499, "t": 651.56638, "r": 559.27808, "b": 674.01989, "coord_origin": "TOPLEFT"}, "confidence": 0.9142336249351501, "cells": [{"id": 203, "text": "[12]", "bbox": {"l": 317.95499, "t": 651.56638, "r": 329.41763, "b": 658.0799, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross B. Girshick. Mask R-CNN.", "bbox": {"l": 331.16287, "t": 651.56638, "r": 559.27808, "b": 658.0799, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "In", "bbox": {"l": 333.39099, "t": 659.53638, "r": 339.35904, "b": 666.0499, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "IEEE International Conference on Computer Vision", "bbox": {"l": 341.56299, "t": 659.57124, "r": 485.8273, "b": 666.05687, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ", ICCV, pages 2980-2988.", "bbox": {"l": 485.82901, "t": 659.53638, "r": 559.27356, "b": 666.0499, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "IEEE Computer Society, Oct 2017.", "bbox": {"l": 333.39099, "t": 667.50636, "r": 429.30161000000004, "b": 674.01989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[12] Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross B. Girshick. Mask R-CNN. In IEEE International Conference on Computer Vision , ICCV, pages 2980-2988. IEEE Computer Society, Oct 2017."}, {"label": "list_item", "id": 23, "page_no": 7, "cluster": {"id": 23, "label": "list_item", "bbox": {"l": 317.95499, "t": 675.47636, "r": 558.97156, "b": 705.900894, "coord_origin": "TOPLEFT"}, "confidence": 0.8895393013954163, "cells": [{"id": 209, "text": "[13]", "bbox": {"l": 317.95499, "t": 675.47636, "r": 330.11407, "b": 681.9898900000001, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012,", "bbox": {"l": 331.96533, "t": 675.47636, "r": 558.96716, "b": 681.9898900000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "bbox": {"l": 333.18201, "t": 683.44637, "r": 558.96661, "b": 689.95989, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "bbox": {"l": 333.39099, "t": 691.41737, "r": 558.97156, "b": 697.930893, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "bbox": {"l": 333.39099, "t": 699.387367, "r": 558.20001, "b": 705.900894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu"}], "body": [{"label": "section_header", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319990873336792, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 CONCLUSION"}, {"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64868, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9824119210243225, "cells": [{"id": 2, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.32153, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 295.64868, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "cLayNet data-sets. By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. 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We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9676095843315125, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table", "bbox": {"l": 331.53516, "t": 348.70233, "r": 558.19904, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "competition. In", "bbox": {"l": 333.39099, "t": 356.67236, "r": 379.36414, "b": 363.18591, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "2013 12th International Conference on Document Analysis and", "bbox": {"l": 381.37201, "t": 356.70724, "r": 558.20099, "b": 363.19287, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Recognition", "bbox": {"l": 333.39099, "t": 364.67724999999996, "r": 365.59601, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ", pages 1449-1453, 2013.", "bbox": {"l": 365.59601, "t": 364.64236, "r": 434.29489, "b": 371.15591, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. 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In", "bbox": {"l": 333.39099, "t": 388.55236999999994, "r": 367.4339, "b": 395.06592, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "2017 14th IAPR International Conference on Document Analysis and", "bbox": {"l": 369.17401, "t": 388.58725000000004, "r": 558.20422, "b": 395.07288, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Recognition (ICDAR)", "bbox": {"l": 333.39099, "t": 396.55725, "r": 390.87601, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": ", volume 01, pages 1404-1410, 2017.", "bbox": {"l": 390.87698, "t": 396.52237, "r": 492.17831, "b": 403.03592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts rdcl2017. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) , volume 01, pages 1404-1410, 2017."}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 390.92053, "r": 294.04712, "b": 443.14014, "coord_origin": "TOPLEFT"}, "confidence": 0.9839397668838501, "cells": [{"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet\u2019s other labels as specified in table 3, and also PubLayNet\u2019s List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "list_item", "id": 17, "page_no": 7, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 321.198, "t": 404.49237, "r": 558.20013, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9257484078407288, "cells": [{"id": 148, "text": "[3]", "bbox": {"l": 321.198, "t": 404.49237, "r": 329.53583, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian", "bbox": {"l": 331.30682, "t": 404.49237, "r": 558.19958, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and", "bbox": {"l": 333.39099, "t": 412.46335, "r": 558.20013, "b": 418.9769, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "Recognition (cTDaR), April 2019. http://sac.founderit.com/.", "bbox": {"l": 333.39099, "t": 420.43335, "r": 501.80127, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[3] Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and Recognition (cTDaR), April 2019. http://sac.founderit.com/."}, {"label": "list_item", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "list_item", "bbox": {"l": 321.198, "t": 428.40335, "r": 559.37872, "b": 458.8269, "coord_origin": "TOPLEFT"}, "confidence": 0.9270963668823242, "cells": [{"id": 152, "text": "[4]", "bbox": {"l": 321.198, "t": 428.40335, "r": 329.91299, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on", "bbox": {"l": 331.7641, "t": 428.40335, "r": 558.19885, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "scientific literature parsing. In", "bbox": {"l": 333.39099, "t": 436.3733500000001, "r": 423.77225, "b": 442.8869, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Proceedings of the International Conference on", "bbox": {"l": 425.909, "t": 436.40823, "r": 558.20178, "b": 442.89386, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 444.37823, "r": 429.42697, "b": 450.86386, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": ", ICDAR, pages 605-617. LNCS 12824, Springer-", "bbox": {"l": 429.42400999999995, "t": 444.34335, "r": 559.37872, "b": 450.85689999999994, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Verlag, sep 2021.", "bbox": {"l": 332.819, "t": 452.31335, "r": 380.01764, "b": 458.8269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[4] Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on scientific literature parsing. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 605-617. LNCS 12824, SpringerVerlag, sep 2021."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "list_item", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 321.198, "t": 460.28336, "r": 559.02625, "b": 490.70792, "coord_origin": "TOPLEFT"}, "confidence": 0.9289959073066711, "cells": [{"id": 159, "text": "[5]", "bbox": {"l": 321.198, "t": 460.28336, "r": 329.22977, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin,", "bbox": {"l": 330.93576, "t": 460.28336, "r": 558.97156, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis:", "bbox": {"l": 333.39099, "t": 468.25336, "r": 559.02625, "b": 474.76691, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "not dead yet.", "bbox": {"l": 333.39099, "t": 476.22437, "r": 368.85431, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "International Journal on Document Analysis and Recognition (IJDAR)", "bbox": {"l": 370.811, "t": 476.25925, "r": 557.46326, "b": 482.74487, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": ",", "bbox": {"l": 557.46503, "t": 476.22437, "r": 558.96857, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "pages 1-11, 01 2022.", "bbox": {"l": 333.39099, "t": 484.19437, "r": 390.82715, "b": 490.70792, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[5] Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin, Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis: not dead yet. 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In", "bbox": {"l": 333.39099, "t": 500.13437, "r": 438.6101100000001, "b": 506.64792, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "Proceedings of the International Conference", "bbox": {"l": 440.349, "t": 500.16925, "r": 558.19958, "b": 506.65488, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "on Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 508.13925, "r": 441.40118, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": ", ICDAR, pages 1015-1022, sep 2019.", "bbox": {"l": 441.4019799999999, "t": 508.10437, "r": 544.78162, "b": 514.61792, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[6] Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset ever for document layout analysis. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 1015-1022, sep 2019."}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 321.19797, "t": 516.07437, "r": 558.9715, "b": 554.46889, "coord_origin": "TOPLEFT"}, "confidence": 0.9426887035369873, "cells": [{"id": 172, "text": "[7]", "bbox": {"l": 321.19797, "t": 516.07437, "r": 329.74161, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and", "bbox": {"l": 331.55634, "t": 516.07437, "r": 558.19897, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In", "bbox": {"l": 333.39099, "t": 524.0443700000001, "r": 558.19891, "b": 530.55792, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "Proceedings of the 28th International Conference on Computational Linguistics", "bbox": {"l": 333.39099, "t": 532.04922, "r": 557.40228, "b": 538.53487, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": ",", "bbox": {"l": 557.40399, "t": 532.01437, "r": 558.96893, "b": 538.5278900000001, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "COLING, pages 949-960. International Committee on Computational Linguistics,", "bbox": {"l": 333.39099, "t": 539.98438, "r": 558.9715, "b": 546.49789, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "dec 2020.", "bbox": {"l": 333.39099, "t": 547.95535, "r": 359.31955, "b": 554.46889, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[7] Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and Ming Zhou. Docbank: A benchmark dataset for document layout analysis. 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In", "bbox": {"l": 333.39099, "t": 563.89536, "r": 535.54352, "b": 570.40889, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "SemWe-", "bbox": {"l": 536.96399, "t": 563.93024, "r": 558.90222, "b": 570.4158600000001, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "bEval@ESWC", "bbox": {"l": 333.39099, "t": 571.9002399999999, "r": 371.94217, "b": 578.38586, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": ", 2016.", "bbox": {"l": 371.94299, "t": 571.86536, "r": 389.72617, "b": 578.3788900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[8] Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction from pdf sources based on rule-based system using integrated formats. 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In", "bbox": {"l": 333.39099, "t": 587.8053600000001, "r": 543.05487, "b": 594.31889, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "IEEE", "bbox": {"l": 544.98499, "t": 587.84024, "r": 558.20148, "b": 594.32587, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Conference on Computer Vision and Pattern Recognition", "bbox": {"l": 333.39099, "t": 595.81024, "r": 491.61166, "b": 602.29587, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": ", CVPR, pages 580-587.", "bbox": {"l": 491.61301, "t": 595.77536, "r": 559.27448, "b": 602.28889, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "IEEE Computer Society, jun 2014.", "bbox": {"l": 333.39099, "t": 603.74536, "r": 428.59726000000006, "b": 610.2589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[9] Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. 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IEEE Computer Society, dec 2015."}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859862327575684, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "list_item", "id": 20, "page_no": 7, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 317.95499, "t": 627.65637, "r": 558.20142, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9127580523490906, "cells": [{"id": 197, "text": "[11]", "bbox": {"l": 317.95499, "t": 627.65637, "r": 329.5459, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 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Nassar, and Peter Staar"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "4bed2a8aa51ac37058e79605821bbc426d032b0b6ca8bdf3409ed8508ccd8c67", "bbox": {"l": 231.8804, "t": 301.50543, "r": 235.14504999999997, "b": 414.69144, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "2f2a06d08f5ad565d0f5e815f4ddf666365b2cff435cdaeb8850217e8a8efabf", "bbox": {"l": 395.06876, "t": 117.37183000000005, "r": 398.33353, "b": 230.55786, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "7f2fd7293e04bf4f1756ae51f5779764933da1d1d2002e3915356050570fc75b", "bbox": {"l": 55.775887, "t": 301.50543, "r": 59.04052000000001, "b": 414.69144, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "1b81cf65f47456ad4faa725d1eb09879bd633af16cfe2bf8cea661b87907bfac", "bbox": {"l": 232.01364, "t": 117.37183000000005, "r": 235.27841000000004, "b": 230.55786, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "b60da9d26f488cb133e47d101d35fda1bdca2671ade60764d1cd569590270327", "bbox": {"l": 395.20047, "t": 301.50543, "r": 398.46512, "b": 414.69144, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "2b7b8355a42ebef0cf91583aad9f30f7c9fa63c5b05911730ba15275c024965b$^{A}$", "bbox": {"l": 55.775818, "t": 117.37183000000005, "r": 65.409912, "b": 230.55786, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "B", "bbox": {"l": 234.56980999999996, "t": 88.50183000000015, "r": 240.06987, "b": 97.01098999999988, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 397.81934, "t": 88.89355, "r": 403.3194, "b": 97.40270999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "D", "bbox": {"l": 59.909843, "t": 266.75885000000005, "r": 65.409912, "b": 275.26793999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "E", "bbox": {"l": 234.77386, "t": 266.36707, "r": 239.85495000000003, "b": 274.87616, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "F", "bbox": {"l": 398.26144, "t": 266.75885000000005, "r": 402.91592, "b": 275.26793999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Text", "bbox": {"l": 62.323874999999994, "t": 442.28543, "r": 70.895882, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Caption", "bbox": {"l": 80.16581, "t": 442.28543, "r": 95.565453, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "List-Item", "bbox": {"l": 104.94447, "t": 442.28543, "r": 122.38113000000001, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Formula", "bbox": {"l": 131.78354, "t": 442.28543, "r": 148.34625, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Table", "bbox": {"l": 157.66106, "t": 442.28543, "r": 168.53032, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Section-Header", "bbox": {"l": 201.24315, "t": 442.28543, "r": 232.00499, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Picture", "bbox": {"l": 177.8381, "t": 442.28543, "r": 191.88956, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Page-Header", "bbox": {"l": 240.95844000000002, "t": 442.28543, "r": 266.61908, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Page-Footer", "bbox": {"l": 276.03928, "t": 442.28543, "r": 300.33261, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Title", "bbox": {"l": 309.74615, "t": 442.28543, "r": 318.50473, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on", "bbox": {"l": 53.79800000000001, "t": 464.48199, "r": 558.203, "b": 472.95523, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demon-", "bbox": {"l": 53.79800000000001, "t": 475.44101, "r": 559.80786, "b": 483.91425, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "strates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion", "bbox": {"l": 53.79800000000001, "t": 486.39999, "r": 558.20294, "b": 494.87323, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "and missing boxes.", "bbox": {"l": 53.79800000000001, "t": 497.358, "r": 130.37105, "b": 505.83124, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ul-", "bbox": {"l": 69.234001, "t": 527.06635, "r": 295.22406, "b": 533.5799, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow", "bbox": {"l": 69.234001, "t": 535.03638, "r": 294.30612, "b": 541.5499, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "export, opencv dnn support, October 2021.", "bbox": {"l": 69.234001, "t": 543.00638, "r": 190.45259, "b": 549.5199, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "[14]", "bbox": {"l": 53.79800000000001, "t": 550.97638, "r": 65.286942, "b": 557.4899, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander", "bbox": {"l": 67.036171, "t": 550.97638, "r": 294.17709, "b": 557.4899, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Kirillov, and Sergey Zagoruyko. 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Asso-", "bbox": {"l": 144.908, "t": 694.4383700000001, "r": 295.22174, "b": 700.951897, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ciation for Computing Machinery.", "bbox": {"l": 69.234001, "t": 702.408363, "r": 166.37207, "b": 708.92189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 1192-1200, New York, USA, 2020. Association for Computing Machinery."}], "headers": [{"label": "page_header", "id": 13, "page_no": 8, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8021655082702637, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8429455161094666, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}]
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Nassar", "bbox": {"l": 182.26804, "t": 192.05737, "r": 265.39255, "b": 203.22357, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "IBM Research", "bbox": {"l": 195.87103, "t": 205.40923999999995, "r": 251.98038999999997, "b": 214.71429, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Rueschlikon, Switzerland", "bbox": {"l": 172.54303, "t": 217.36425999999994, "r": 275.30725, "b": 226.66931, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "ahn@zurich.ibm.com", "bbox": {"l": 180.52803, "t": 229.32025, "r": 267.3222, "b": 238.62531, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Peter Staar", "bbox": {"l": 361.52802, "t": 192.05737, "r": 414.84821, "b": 203.22357, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "IBM Research", "bbox": {"l": 360.02002, "t": 205.40923999999995, "r": 416.12939, "b": 214.71429, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Rueschlikon, Switzerland", "bbox": {"l": 336.69302, "t": 217.36425999999994, "r": 439.45727999999997, "b": 226.66931, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "taa@zurich.ibm.com", "bbox": {"l": 346.20703, "t": 229.32025, "r": 429.94269, "b": 238.62531, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "ABSTRACT", "bbox": {"l": 53.798035, "t": 247.70288000000005, "r": 111.94354, "b": 258.01202, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Accurate document layout analysis is a key requirement for high-", "bbox": {"l": 53.484001, "t": 262.90454, "r": 295.55591, "b": 271.27917, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "quality PDF document conversion. With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "13", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "USING THE VERTICAL TUBE -", "bbox": {"l": 327.83005, "t": 331.57268999999997, "r": 351.16092, "b": 333.31171, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "MODELS AY11230/11234", "bbox": {"l": 327.83005, "t": 333.18292, "r": 348.30536, "b": 334.92194, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, 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The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 239, "label": "text", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 240, "label": "text", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 295, "text": "the landing surface. 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "caption", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 559.51524, "coord_origin": "TOPLEFT"}, "confidence": 0.7793391346931458, "cells": [{"id": 409, "text": "Figure 1: Four examples of complex page layouts across dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458844423294067, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; 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While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "caption", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 559.51524, "coord_origin": "TOPLEFT"}, "confidence": 0.7793391346931458, "cells": [{"id": 409, "text": "Figure 1: Four examples of complex page layouts across dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Four examples of complex page layouts across different document categories"}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458844423294067, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; 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Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. 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Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Washington, DC, USA. 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "caption", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 559.51524, "coord_origin": "TOPLEFT"}, "confidence": 0.7793391346931458, "cells": [{"id": 409, "text": "Figure 1: Four examples of complex page layouts across dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Four examples of complex page layouts across different document categories"}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458844423294067, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; 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\u2022 Applied computing \u2192 Document analysis ; \u2022 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9208475351333618, "cells": [{"id": 411, "text": "KEYWORDS", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KEYWORDS"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 627.00117, "coord_origin": "TOPLEFT"}, "confidence": 0.9509093761444092, "cells": [{"id": 412, "text": "PDF document conversion, layout segmentation, object-detection,", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 616.04218, "coord_origin": "TOPLEFT"}}, {"id": 413, "text": "data set, Machine Learning", "bbox": {"l": 317.95499, "t": 618.62656, "r": 416.94403, "b": 627.00117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning"}, {"label": "text", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "text", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 295.11798, "b": 672.79189, "coord_origin": "TOPLEFT"}, "confidence": 0.7107337117195129, "cells": [{"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.65997, "t": 640.05434, "r": 404.65366, "b": 647.58609, "coord_origin": "TOPLEFT"}, "confidence": 0.8721982836723328, "cells": [{"id": 414, "text": "ACM Reference Format:", "bbox": {"l": 317.65997, "t": 640.05434, "r": 404.65366, "b": 647.58609, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACM Reference Format:"}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 650.11996, "r": 559.5495, "b": 707.377029, "coord_origin": "TOPLEFT"}, "confidence": 0.9455163478851318, "cells": [{"id": 415, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter", "bbox": {"l": 317.95499, "t": 650.11996, "r": 558.35266, "b": 657.56404, "coord_origin": "TOPLEFT"}}, {"id": 416, "text": "Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for Document-", "bbox": {"l": 317.95499, "t": 660.08296, "r": 559.5495, "b": 667.52703, "coord_origin": "TOPLEFT"}}, {"id": 417, "text": "Layout Analysis. In", "bbox": {"l": 317.95499, "t": 670.04497, "r": 383.30807, "b": 677.48904, "coord_origin": "TOPLEFT"}}, {"id": 418, "text": "Proceedings of the 28th ACM SIGKDD Conference on", "bbox": {"l": 385.798, "t": 670.08482, "r": 558.20032, "b": 677.49701, "coord_origin": "TOPLEFT"}}, {"id": 419, "text": "Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Wash-", "bbox": {"l": 317.95499, "t": 680.04781, "r": 559.00092, "b": 687.46001, "coord_origin": "TOPLEFT"}}, {"id": 420, "text": "ington, DC, USA.", "bbox": {"l": 317.95499, "t": 690.01081, "r": 370.11481, "b": 697.423004, "coord_origin": "TOPLEFT"}}, {"id": 421, "text": "ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/", "bbox": {"l": 371.82999, "t": 689.97096, "r": 558.71655, "b": 697.415031, "coord_origin": "TOPLEFT"}}, {"id": 422, "text": "3534678.3539043", "bbox": {"l": 317.95499, "t": 699.932953, "r": 371.59375, "b": 707.377029, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Washington, DC, USA. 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Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680323123931885, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367701411247253, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996682524681091, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279747962952, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813894629478455, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844210147857666, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792873859405518, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347410202026367, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795214533805847, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330971837043762, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019714355469, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594184160232544, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615143537521362, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447574615478516, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749842882156372, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586857914924622, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680323123931885, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367701411247253, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996682524681091, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279747962952, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813894629478455, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844210147857666, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792873859405518, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347410202026367, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795214533805847, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330971837043762, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019714355469, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594184160232544, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615143537521362, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447574615478516, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749842882156372, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586857914924622, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}], "body": [{"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367701411247253, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996682524681091, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279747962952, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813894629478455, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844210147857666, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792873859405518, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347410202026367, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795214533805847, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330971837043762, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019714355469, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594184160232544, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615143537521362, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447574615478516, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749842882156372, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586857914924622, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}], "headers": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680323123931885, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920252323150635, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.91046142578125, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "picture", "bbox": {"l": 88.32998657226562, "t": 92.88650512695312, "r": 263.70513916015625, "b": 220.56797790527344, "coord_origin": "TOPLEFT"}, "confidence": 0.9698249101638794, "cells": [], "children": [{"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425569772720337, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048321843147278, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920252323150635, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.91046142578125, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.32998657226562, "t": 92.88650512695312, "r": 263.70513916015625, "b": 220.56797790527344, "coord_origin": "TOPLEFT"}, "confidence": 0.9698249101638794, "cells": [], "children": [{"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 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"TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "caption", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425569772720337, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \u201cinvisible\u201d tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \u201cinvisible\u201d list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \u201ctext in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048321843147278, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}], "body": [{"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.32998657226562, "t": 92.88650512695312, "r": 263.70513916015625, "b": 220.56797790527344, "coord_origin": "TOPLEFT"}, "confidence": 0.9698249101638794, "cells": [], "children": [{"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "caption", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425569772720337, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \u201cinvisible\u201d tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \u201cinvisible\u201d list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \u201ctext in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048321843147278, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}], "headers": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920252323150635, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.91046142578125, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 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"coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 418.16003, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "95-99", "bbox": {"l": 428.1217, "t": 162.53954999999996, "r": 447.82986, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "69-78", "bbox": {"l": 457.80051, "t": 162.53954999999996, "r": 477.50867, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "n/a", "bbox": {"l": 495.32489, "t": 162.53954999999996, "r": 507.17846999999995, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Footnote", "bbox": {"l": 104.825, "t": 173.49854000000005, "r": 137.3282, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "6318", "bbox": {"l": 182.035, "t": 173.49854000000005, "r": 198.71251, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "0.60", "bbox": {"l": 219.211, "t": 173.49854000000005, "r": 233.69174000000004, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "0.31", "bbox": {"l": 250.01956, "t": 173.49854000000005, "r": 264.50031, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "0.58", "bbox": {"l": 280.82812, "t": 173.49854000000005, "r": 295.30887, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "83-91", "bbox": {"l": 305.27301, "t": 173.49854000000005, "r": 324.98117, "b": 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"coord_origin": "TOPLEFT"}}, {"id": 162, "text": "68-85", "bbox": {"l": 487.47034, "t": 283.48654, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625211477279663, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906538486480713, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 98.93107604980469, "t": 137.4754638671875, "r": 512.5799560546875, "b": 294.08154296875, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 418.16003, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "95-99", "bbox": {"l": 428.1217, "t": 162.53954999999996, "r": 447.82986, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "69-78", "bbox": {"l": 457.80051, "t": 162.53954999999996, "r": 477.50867, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "n/a", "bbox": {"l": 495.32489, "t": 162.53954999999996, "r": 507.17846999999995, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Footnote", "bbox": {"l": 104.825, "t": 173.49854000000005, "r": 137.3282, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "6318", "bbox": {"l": 182.035, "t": 173.49854000000005, "r": 198.71251, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "0.60", "bbox": {"l": 219.211, "t": 173.49854000000005, "r": 233.69174000000004, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "0.31", "bbox": {"l": 250.01956, "t": 173.49854000000005, "r": 264.50031, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "0.58", "bbox": {"l": 280.82812, "t": 173.49854000000005, "r": 295.30887, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "83-91", "bbox": {"l": 305.27301, "t": 173.49854000000005, "r": 324.98117, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "n/a", "bbox": {"l": 342.79739, "t": 173.49854000000005, "r": 354.65097, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "100", "bbox": {"l": 371.81265, "t": 173.49854000000005, "r": 384.32077, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "62-88", "bbox": {"l": 398.45181, "t": 173.49854000000005, "r": 418.15997, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "85-94", "bbox": {"l": 428.12164, "t": 173.49854000000005, "r": 447.8298, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "n/a", "bbox": {"l": 465.655, "t": 173.49854000000005, "r": 477.50857999999994, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "82-97", "bbox": {"l": 487.47025, "t": 173.49854000000005, "r": 507.17841, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Formula", "bbox": {"l": 104.825, "t": 184.45752000000005, "r": 135.33766, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "25027", "bbox": {"l": 177.866, "t": 184.45752000000005, "r": 198.71288, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "2.25", "bbox": {"l": 219.211, "t": 184.45752000000005, "r": 233.69174000000004, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1.90", "bbox": {"l": 250.01956, "t": 184.45752000000005, "r": 264.50031, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "2.96", "bbox": {"l": 280.82812, "t": 184.45752000000005, "r": 295.30887, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "83-85", "bbox": {"l": 305.27301, "t": 184.45752000000005, "r": 324.98117, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "n/a", "bbox": {"l": 342.79739, "t": 184.45752000000005, "r": 354.65097, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "n/a", "bbox": {"l": 372.46719, "t": 184.45752000000005, "r": 384.32077, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "84-87", "bbox": {"l": 398.45181, "t": 184.45752000000005, "r": 418.15997, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "86-96", "bbox": {"l": 428.12164, "t": 184.45752000000005, "r": 447.8298, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "n/a", "bbox": {"l": 465.655, "t": 184.45752000000005, "r": 477.50857999999994, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "n/a", "bbox": {"l": 495.3248, "t": 184.45752000000005, "r": 507.17838000000006, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "List-item", "bbox": {"l": 104.825, 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Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847549200057983, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64874, "b": 606.31924, "coord_origin": "TOPLEFT"}, "confidence": 0.8104852437973022, "cells": [{"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.98039311170578, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.974835991859436, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"1": {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93107604980469, "t": 137.4754638671875, "r": 512.5799560546875, "b": 294.08154296875, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 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"assembled": {"elements": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625211477279663, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906538486480713, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93107604980469, "t": 137.4754638671875, "r": 512.5799560546875, "b": 294.08154296875, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, 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"predicted_class": null, "confidence": null}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818442463874817, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847549200057983, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64874, "b": 606.31924, "coord_origin": "TOPLEFT"}, "confidence": 0.8104852437973022, "cells": [{"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.98039311170578, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.974835991859436, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{3}$https://arxiv.org/"}], "body": [{"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906538486480713, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93107604980469, "t": 137.4754638671875, "r": 512.5799560546875, "b": 294.08154296875, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, 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"predicted_class": null, "confidence": null}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818442463874817, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847549200057983, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64874, "b": 606.31924, "coord_origin": "TOPLEFT"}, "confidence": 0.8104852437973022, "cells": [{"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.98039311170578, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.974835991859436, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{3}$https://arxiv.org/"}], "headers": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625211477279663, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.887782871723175, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534672856330872, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264117002487183, "cells": [], "children": [{"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, 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"the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691525101661682, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873710632324219, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544305205345154, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611717462539673, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.982954204082489, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.887782871723175, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534672856330872, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264117002487183, "cells": [], "children": [{"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 84, "text": 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326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.902275800704956, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691525101661682, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "text", "id": 22, "page_no": 4, "cluster": {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"label": "caption", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873710632324219, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544305205345154, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611717462539673, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.982954204082489, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other\u2019s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}], "body": [{"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264117002487183, "cells": [], "children": [{"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.902275800704956, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691525101661682, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "text", "id": 22, "page_no": 4, "cluster": {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"label": "caption", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873710632324219, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544305205345154, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611717462539673, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.982954204082489, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other\u2019s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}], "headers": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.887782871723175, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534672856330872, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "human", "bbox": {"l": 132.36501, "t": 197.97351000000003, "r": 157.99098, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "MRCNN", "bbox": {"l": 173.505, "t": 197.97351000000003, "r": 204.61841, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "FRCNN", "bbox": {"l": 220.13028, "t": 197.97351000000003, "r": 248.06958, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "YOLO", "bbox": {"l": 258.03125, "t": 197.97351000000003, "r": 280.17825, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "R50", "bbox": {"l": 168.39301, "t": 208.93255999999997, "r": 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{"id": 68, "text": "74.6", "bbox": {"l": 261.86804, "t": 297.00253, "r": 276.34879, "b": 305.37717, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Table", "bbox": {"l": 67.663002, "t": 307.96155, "r": 87.46978, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "77-81", "bbox": {"l": 135.32401, "t": 307.96155, "r": 155.03215, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "82.2", "bbox": {"l": 167.95399, "t": 307.96155, "r": 182.43472, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "82.9", "bbox": {"l": 194.0462, "t": 307.96155, "r": 208.52695, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "82.2", "bbox": {"l": 226.86324000000002, "t": 307.96155, "r": 241.34396, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "86.3", "bbox": {"l": 261.86804, "t": 307.96155, "r": 276.34879, "b": 316.33618, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Text", "bbox": {"l": 67.663002, "t": 318.91953, "r": 83.623199, "b": 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167.95399, "t": 329.87854, "r": 182.43472, "b": 338.25317, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "80.4", "bbox": {"l": 194.0462, "t": 329.87854, "r": 208.52695, "b": 338.25317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "79.9", "bbox": {"l": 226.86324000000002, "t": 329.87854, "r": 241.34396, "b": 338.25317, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "82.7", "bbox": {"l": 261.86804, "t": 329.87854, "r": 276.34879, "b": 338.25317, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "All", "bbox": {"l": 67.663002, "t": 341.23654, "r": 78.628906, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "82-83", "bbox": {"l": 135.32401, "t": 341.23654, "r": 155.03215, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "72.4", "bbox": {"l": 167.95399, "t": 341.23654, "r": 182.43472, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "73.5", "bbox": {"l": 194.0462, "t": 341.23654, "r": 208.52695, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "73.4", "bbox": {"l": 226.86324000000002, "t": 341.23654, "r": 241.34396, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "76.8", "bbox": {"l": 261.86804, "t": 341.23654, "r": 276.34879, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "0", "bbox": {"l": 349.16577, "t": 246.68017999999995, "r": 352.48175, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "20", "bbox": {"l": 385.93698, "t": 246.68017999999995, "r": 392.56894, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "40", "bbox": {"l": 424.366, "t": 246.68017999999995, "r": 430.99796, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "60", "bbox": {"l": 462.79504000000003, "t": 246.68017999999995, "r": 469.427, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "80", "bbox": {"l": 501.22406, "t": 246.68017999999995, "r": 507.85602, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "100", "bbox": {"l": 537.99524, "t": 246.68017999999995, "r": 547.94318, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "% of DocLayNet training set", "bbox": {"l": 410.28143, "t": 253.80840999999998, "r": 483.47278000000006, "b": 259.88251, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "50", "bbox": {"l": 330.93539, "t": 218.38464, "r": 337.56735, "b": 224.45874000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "55", "bbox": {"l": 330.93539, "t": 192.08660999999995, "r": 337.56735, "b": 198.16071, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "60", "bbox": {"l": 330.93539, "t": 165.78864, "r": 337.56735, "b": 171.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "65", "bbox": {"l": 330.93539, "t": 139.49059999999997, "r": 337.56735, "b": 145.56470000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "70", "bbox": {"l": 330.93539, "t": 113.19263000000001, "r": 337.56735, "b": 119.26671999999996, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "mAP 0.50:0.95", "bbox": {"l": 322.92276, "t": 148.37689, "r": 328.99686, "b": 186.79218000000003, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "10", "bbox": {"l": 470.97235, "t": 235.36676, "r": 477.6055, "b": 241.44086000000004, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "1", "bbox": {"l": 477.65662, "t": 234.82390999999996, "r": 479.97778000000005, "b": 239.07581000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "10", "bbox": {"l": 531.55127, "t": 235.41234999999995, "r": 538.18445, "b": 241.48645, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "2", "bbox": {"l": 538.23553, "t": 234.86951, "r": 540.5567, "b": 239.1214, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "50", "bbox": {"l": 404.91125, "t": 216.00005999999996, "r": 411.54321, "b": 222.07416, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "55", "bbox": {"l": 404.91125, "t": 200.22125000000005, "r": 411.54321, "b": 206.29534999999998, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "60", "bbox": {"l": 404.91125, "t": 184.44244000000003, "r": 411.54321, "b": 190.51653999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "65", "bbox": {"l": 404.91125, "t": 168.66364, "r": 411.54321, "b": 174.73773000000006, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "70", "bbox": {"l": 404.91125, "t": 152.88489000000004, "r": 411.54321, "b": 158.95898, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. 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The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.528999, "t": 370.92755, "r": 295.55612, "b": 576.56317, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579004049301147, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.9876052141189575, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "table", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "table", "bbox": {"l": 62.02751922607422, "t": 195.68003845214844, "r": 285.78955078125, "b": 351.6618957519531, "coord_origin": "TOPLEFT"}, 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The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.528999, "t": 370.92755, "r": 295.55612, "b": 576.56317, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579004049301147, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.9876052141189575, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}], "body": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64874, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9782734513282776, "cells": [{"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. 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"end_col_offset_idx": 6, "text": "76.8", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 317.95499, "t": 279.01599, "r": 559.80579, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.963992178440094, "cells": [{"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.528999, "t": 370.92755, "r": 295.55612, "b": 576.56317, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579004049301147, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.9876052141189575, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}], "headers": [{"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8662774562835693, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. 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"b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "72", "bbox": {"l": 151.07401, "t": 285.40454, "r": 159.41275, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "73", "bbox": {"l": 177.23795, "t": 285.40454, "r": 185.57669, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "78", "bbox": {"l": 211.25645, "t": 285.40454, "r": 219.5952, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "77", "bbox": {"l": 245.27496, "t": 285.40454, "r": 253.61371, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Class-count", "bbox": {"l": 358.63901, "t": 153.10051999999996, "r": 401.73154, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "11", "bbox": {"l": 440.22501, "t": 153.10051999999996, "r": 448.56375, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "5", "bbox": {"l": 494.38, "t": 153.10051999999996, "r": 498.54938, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Split", "bbox": {"l": 358.63901, "t": 164.05951000000005, "r": 375.27167, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Doc", "bbox": {"l": 423.341, "t": 164.05951000000005, "r": 438.0459, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Page", "bbox": {"l": 448.00757, "t": 164.05951000000005, "r": 465.4472, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Doc", "bbox": {"l": 475.41101, "t": 164.05951000000005, "r": 490.11591, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Page", "bbox": {"l": 500.07757999999995, "t": 164.05951000000005, "r": 517.51721, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Caption", "bbox": {"l": 358.63901, "t": 175.41656, "r": 387.82465, "b": 183.7912, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "68", "bbox": {"l": 426.52399, "t": 175.41656, "r": 434.86273, "b": 183.7912, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "83", "bbox": {"l": 452.56240999999994, "t": 175.41656, "r": 460.90115000000003, "b": 183.7912, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Footnote", "bbox": {"l": 358.63901, "t": 186.37554999999998, "r": 391.14221, "b": 194.75018, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "71", 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"coord_origin": "TOPLEFT"}}, {"id": 168, "text": "84", "bbox": {"l": 452.56240999999994, "t": 296.36255, "r": 460.90115000000003, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "78", "bbox": {"l": 478.59399, "t": 296.36255, "r": 486.93274, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "87", "bbox": {"l": 504.6324200000001, "t": 296.36255, "r": 512.97119, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9316117763519287, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.8296932578086853, "cells": [{"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.87362140417099, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 352.97747802734375, "t": 150.79122924804688, "r": 522.9158935546875, "b": 306.265869140625, "coord_origin": "TOPLEFT"}, "confidence": 0.9879695177078247, "cells": [{"id": 115, "text": "Class-count", "bbox": {"l": 358.63901, "t": 153.10051999999996, "r": 401.73154, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "11", "bbox": {"l": 440.22501, "t": 153.10051999999996, "r": 448.56375, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "5", "bbox": {"l": 494.38, "t": 153.10051999999996, "r": 498.54938, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Split", "bbox": {"l": 358.63901, "t": 164.05951000000005, "r": 375.27167, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Doc", "bbox": {"l": 423.341, "t": 164.05951000000005, "r": 438.0459, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Page", "bbox": {"l": 448.00757, "t": 164.05951000000005, "r": 465.4472, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Doc", "bbox": {"l": 475.41101, "t": 164.05951000000005, "r": 490.11591, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Page", "bbox": {"l": 500.07757999999995, "t": 164.05951000000005, "r": 517.51721, "b": 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{"l": 504.6324200000001, "t": 208.29351999999994, "r": 512.97119, "b": 216.66814999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 358.63901, "t": 219.25256000000002, "r": 401.16666, "b": 227.62720000000002, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "62", "bbox": {"l": 426.52399, "t": 219.25256000000002, "r": 434.86273, "b": 227.62720000000002, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "89", "bbox": {"l": 452.56240999999994, "t": 219.25256000000002, "r": 460.90115000000003, "b": 227.62720000000002, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Page-header", "bbox": {"l": 358.63901, "t": 230.21155, "r": 403.91931, "b": 238.58618, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "72", "bbox": {"l": 426.52399, "t": 230.21155, "r": 434.86273, "b": 238.58618, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "90", "bbox": {"l": 452.56240999999994, "t": 230.21155, "r": 460.90115000000003, "b": 238.58618, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Picture", "bbox": {"l": 358.63901, "t": 241.16956000000005, "r": 384.62366, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "72", "bbox": {"l": 426.52399, "t": 241.16956000000005, "r": 434.86273, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "82", "bbox": {"l": 452.56240999999994, "t": 241.16956000000005, "r": 460.90115000000003, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "72", "bbox": {"l": 478.59399, "t": 241.16956000000005, "r": 486.93274, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "82", "bbox": {"l": 504.6324200000001, "t": 241.16956000000005, "r": 512.97119, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Section-header", "bbox": {"l": 358.63901, "t": 252.12854000000004, "r": 413.37891, "b": 260.50316999999995, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "68", "bbox": {"l": 426.52399, "t": 252.12854000000004, "r": 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The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572024941444397, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589216113090515, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 352.97747802734375, "t": 150.79122924804688, "r": 522.9158935546875, "b": 306.265869140625, "coord_origin": "TOPLEFT"}, "confidence": 0.9879695177078247, "cells": [{"id": 115, "text": "Class-count", "bbox": {"l": 358.63901, "t": 153.10051999999996, "r": 401.73154, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "11", "bbox": {"l": 440.22501, "t": 153.10051999999996, "r": 448.56375, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "5", "bbox": {"l": 494.38, "t": 153.10051999999996, "r": 498.54938, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Split", "bbox": {"l": 358.63901, "t": 164.05951000000005, "r": 375.27167, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Doc", "bbox": {"l": 423.341, "t": 164.05951000000005, "r": 438.0459, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Page", "bbox": {"l": 448.00757, "t": 164.05951000000005, "r": 465.4472, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Doc", "bbox": {"l": 475.41101, "t": 164.05951000000005, "r": 490.11591, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Page", "bbox": {"l": 500.07757999999995, "t": 164.05951000000005, "r": 517.51721, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Caption", "bbox": {"l": 358.63901, "t": 175.41656, "r": 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0.9555687308311462, "cells": [{"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Learning Curve"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740145802497864, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "text", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is \u201clarge enough\u201d. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572024941444397, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589216113090515, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "body": [{"label": "text", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.8296932578086853, "cells": [{"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels."}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.87362140417099, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 352.97747802734375, "t": 150.79122924804688, "r": 522.9158935546875, "b": 306.265869140625, "coord_origin": "TOPLEFT"}, "confidence": 0.9879695177078247, "cells": [{"id": 115, "text": "Class-count", "bbox": {"l": 358.63901, "t": 153.10051999999996, "r": 401.73154, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "11", "bbox": {"l": 440.22501, "t": 153.10051999999996, "r": 448.56375, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "5", "bbox": {"l": 494.38, "t": 153.10051999999996, "r": 498.54938, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Split", "bbox": {"l": 358.63901, "t": 164.05951000000005, "r": 375.27167, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Doc", "bbox": {"l": 423.341, "t": 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0.9555687308311462, "cells": [{"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Learning Curve"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740145802497864, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "text", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is \u201clarge enough\u201d. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572024941444397, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589216113090515, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "headers": [{"label": "page_header", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9316117763519287, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.32153, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 295.64868, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "cLayNet data-sets. By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Testing on", "bbox": {"l": 217.74099999999999, "t": 175.01855, "r": 256.26065, "b": 183.39319, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Training on", "bbox": {"l": 89.954002, "t": 185.97655999999995, "r": 133.24379, "b": 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{"id": 66, "text": "59", "bbox": {"l": 208.44701, "t": 329.63855, "r": 216.78575000000004, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "47", "bbox": {"l": 232.1183, "t": 329.63855, "r": 240.45705, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "78", "bbox": {"l": 256.49792, "t": 329.63855, "r": 264.83667, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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In", "bbox": {"l": 333.39099, "t": 563.89536, "r": 535.54352, "b": 570.40889, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "SemWe-", "bbox": {"l": 536.96399, "t": 563.93024, "r": 558.90222, "b": 570.4158600000001, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "bEval@ESWC", "bbox": {"l": 333.39099, "t": 571.9002399999999, "r": 371.94217, "b": 578.38586, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": ", 2016.", "bbox": {"l": 371.94299, "t": 571.86536, "r": 389.72617, "b": 578.3788900000001, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "[9]", "bbox": {"l": 321.198, "t": 579.83536, "r": 329.45999, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature", "bbox": {"l": 331.21487, "t": 579.83536, "r": 558.19843, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "hierarchies for accurate object detection and semantic segmentation. In", "bbox": {"l": 333.39099, "t": 587.8053600000001, "r": 543.05487, "b": 594.31889, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "IEEE", "bbox": {"l": 544.98499, "t": 587.84024, "r": 558.20148, "b": 594.32587, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Conference on Computer Vision and Pattern Recognition", "bbox": {"l": 333.39099, "t": 595.81024, "r": 491.61166, "b": 602.29587, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": ", CVPR, pages 580-587.", "bbox": {"l": 491.61301, "t": 595.77536, "r": 559.27448, "b": 602.28889, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "IEEE Computer Society, jun 2014.", "bbox": {"l": 333.39099, "t": 603.74536, "r": 428.59726000000006, "b": 610.2589, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "[10]", "bbox": {"l": 317.95499, "t": 611.71536, "r": 329.54767, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Ross B. Girshick. Fast R-CNN. In", "bbox": {"l": 331.31268, "t": 611.71536, "r": 425.1554, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "2015 IEEE International Conference on Computer", "bbox": {"l": 426.77802, "t": 611.75024, "r": 558.20203, "b": 618.23587, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Vision", "bbox": {"l": 333.39099, "t": 619.72124, "r": 350.59537, "b": 626.20686, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": ", ICCV, pages 1440-1448. IEEE Computer Society, dec 2015.", "bbox": {"l": 350.59598, "t": 619.68637, "r": 518.58777, "b": 626.19989, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "[11]", "bbox": {"l": 317.95499, "t": 627.65637, "r": 329.5459, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards", "bbox": {"l": 331.31064, "t": 627.65637, "r": 558.20142, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "real-time object detection with region proposal networks.", "bbox": {"l": 333.39099, "t": 635.62637, "r": 497.50909, "b": 642.13989, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "IEEE Transactions on", "bbox": {"l": 500.01401, "t": 635.66124, "r": 558.19885, "b": 642.14687, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Pattern Analysis and Machine Intelligence", "bbox": {"l": 333.39099, "t": 643.6312399999999, "r": 449.38620000000003, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": ", 39(6):1137-1149, 2017.", "bbox": {"l": 449.38699, "t": 643.59637, "r": 515.74268, "b": 650.10989, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "[12]", "bbox": {"l": 317.95499, "t": 651.56638, "r": 329.41763, "b": 658.0799, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross B. Girshick. Mask R-CNN.", "bbox": {"l": 331.16287, "t": 651.56638, "r": 559.27808, "b": 658.0799, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "In", "bbox": {"l": 333.39099, "t": 659.53638, "r": 339.35904, "b": 666.0499, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "IEEE International Conference on Computer Vision", "bbox": {"l": 341.56299, "t": 659.57124, "r": 485.8273, "b": 666.05687, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ", ICCV, pages 2980-2988.", "bbox": {"l": 485.82901, "t": 659.53638, "r": 559.27356, "b": 666.0499, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "IEEE Computer Society, Oct 2017.", "bbox": {"l": 333.39099, "t": 667.50636, "r": 429.30161000000004, "b": 674.01989, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "[13]", "bbox": {"l": 317.95499, "t": 675.47636, "r": 330.11407, "b": 681.9898900000001, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012,", "bbox": {"l": 331.96533, "t": 675.47636, "r": 558.96716, "b": 681.9898900000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "bbox": {"l": 333.18201, "t": 683.44637, "r": 558.96661, "b": 689.95989, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "bbox": {"l": 333.39099, "t": 691.41737, "r": 558.97156, "b": 697.930893, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "bbox": {"l": 333.39099, "t": 699.387367, "r": 558.20001, "b": 705.900894, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 24, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.79841548204422, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319990873336792, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64868, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9824119210243225, "cells": [{"id": 2, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.32153, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 295.64868, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "cLayNet data-sets. By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. 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Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 72.65901184082031, "t": 172.4807891845703, "r": 274.8346862792969, "b": 339.85400390625, "coord_origin": "TOPLEFT"}, "confidence": 0.9892617464065552, "cells": [{"id": 8, "text": "Testing on", "bbox": {"l": 217.74099999999999, "t": 175.01855, "r": 256.26065, "b": 183.39319, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Training on", "bbox": {"l": 89.954002, "t": 185.97655999999995, "r": 133.24379, "b": 194.35119999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "labels", "bbox": {"l": 154.629, "t": 185.97655999999995, "r": 175.47588, "b": 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other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9676095843315125, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table", "bbox": {"l": 331.53516, "t": 348.70233, "r": 558.19904, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "competition. In", "bbox": {"l": 333.39099, "t": 356.67236, "r": 379.36414, "b": 363.18591, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "2013 12th International Conference on Document Analysis and", "bbox": {"l": 381.37201, "t": 356.70724, "r": 558.20099, "b": 363.19287, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Recognition", "bbox": {"l": 333.39099, "t": 364.67724999999996, "r": 365.59601, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ", pages 1449-1453, 2013.", "bbox": {"l": 365.59601, "t": 364.64236, "r": 434.29489, "b": 371.15591, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 321.198, "t": 372.61237, "r": 559.37982, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9480941295623779, "cells": [{"id": 141, "text": "[2]", "bbox": {"l": 321.198, "t": 372.61237, "r": 329.85956, "b": 379.12592, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Ic-", "bbox": {"l": 331.69931, "t": 372.61237, "r": 559.37976, "b": 379.12592, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "dar2017 competition on recognition of documents with complex layouts -", "bbox": {"l": 333.39099, "t": 380.58237, "r": 559.37982, "b": 387.09592, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "rdcl2017. In", "bbox": {"l": 333.39099, "t": 388.55236999999994, "r": 367.4339, "b": 395.06592, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "2017 14th IAPR International Conference on Document Analysis and", "bbox": {"l": 369.17401, "t": 388.58725000000004, "r": 558.20422, "b": 395.07288, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Recognition (ICDAR)", "bbox": {"l": 333.39099, "t": 396.55725, "r": 390.87601, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": ", volume 01, pages 1404-1410, 2017.", "bbox": {"l": 390.87698, "t": 396.52237, "r": 492.17831, "b": 403.03592, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 390.92053, "r": 294.04712, "b": 443.14014, "coord_origin": "TOPLEFT"}, "confidence": 0.9839397668838501, "cells": [{"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 321.198, "t": 404.49237, "r": 558.20013, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9257484078407288, "cells": [{"id": 148, "text": "[3]", "bbox": {"l": 321.198, "t": 404.49237, "r": 329.53583, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian", "bbox": {"l": 331.30682, "t": 404.49237, "r": 558.19958, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and", "bbox": {"l": 333.39099, "t": 412.46335, "r": 558.20013, "b": 418.9769, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "Recognition (cTDaR), April 2019. http://sac.founderit.com/.", "bbox": {"l": 333.39099, "t": 420.43335, "r": 501.80127, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "list_item", "bbox": {"l": 321.198, "t": 428.40335, "r": 559.37872, "b": 458.8269, "coord_origin": "TOPLEFT"}, "confidence": 0.9270963668823242, "cells": [{"id": 152, "text": "[4]", "bbox": {"l": 321.198, "t": 428.40335, "r": 329.91299, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on", "bbox": {"l": 331.7641, "t": 428.40335, "r": 558.19885, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "scientific literature parsing. In", "bbox": {"l": 333.39099, "t": 436.3733500000001, "r": 423.77225, "b": 442.8869, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Proceedings of the International Conference on", "bbox": {"l": 425.909, "t": 436.40823, "r": 558.20178, "b": 442.89386, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 444.37823, "r": 429.42697, "b": 450.86386, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": ", ICDAR, pages 605-617. LNCS 12824, Springer-", "bbox": {"l": 429.42400999999995, "t": 444.34335, "r": 559.37872, "b": 450.85689999999994, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Verlag, sep 2021.", "bbox": {"l": 332.819, "t": 452.31335, "r": 380.01764, "b": 458.8269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "list_item", "bbox": {"l": 321.198, "t": 460.28336, "r": 559.02625, "b": 490.70792, "coord_origin": "TOPLEFT"}, "confidence": 0.9289959073066711, "cells": [{"id": 159, "text": "[5]", "bbox": {"l": 321.198, "t": 460.28336, "r": 329.22977, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin,", "bbox": {"l": 330.93576, "t": 460.28336, "r": 558.97156, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis:", "bbox": {"l": 333.39099, "t": 468.25336, "r": 559.02625, "b": 474.76691, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "not dead yet.", "bbox": {"l": 333.39099, "t": 476.22437, "r": 368.85431, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "International Journal on Document Analysis and Recognition (IJDAR)", "bbox": {"l": 370.811, "t": 476.25925, "r": 557.46326, "b": 482.74487, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": ",", "bbox": {"l": 557.46503, "t": 476.22437, "r": 558.96857, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "pages 1-11, 01 2022.", "bbox": {"l": 333.39099, "t": 484.19437, "r": 390.82715, "b": 490.70792, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "list_item", "bbox": {"l": 321.198, "t": 492.16437, "r": 558.20361, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.927467405796051, "cells": [{"id": 166, "text": "[6]", "bbox": {"l": 321.198, "t": 492.16437, "r": 329.42145, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset", "bbox": {"l": 331.16812, "t": 492.16437, "r": 558.20361, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "ever for document layout analysis. In", "bbox": {"l": 333.39099, "t": 500.13437, "r": 438.6101100000001, "b": 506.64792, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "Proceedings of the International Conference", "bbox": {"l": 440.349, "t": 500.16925, "r": 558.19958, "b": 506.65488, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "on Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 508.13925, "r": 441.40118, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": ", ICDAR, pages 1015-1022, sep 2019.", "bbox": {"l": 441.4019799999999, "t": 508.10437, "r": 544.78162, "b": 514.61792, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 321.19797, "t": 516.07437, "r": 558.9715, "b": 554.46889, "coord_origin": "TOPLEFT"}, "confidence": 0.9426887035369873, "cells": [{"id": 172, "text": "[7]", "bbox": {"l": 321.19797, "t": 516.07437, "r": 329.74161, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and", "bbox": {"l": 331.55634, "t": 516.07437, "r": 558.19897, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In", "bbox": {"l": 333.39099, "t": 524.0443700000001, "r": 558.19891, "b": 530.55792, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "Proceedings of the 28th International Conference on Computational Linguistics", "bbox": {"l": 333.39099, "t": 532.04922, "r": 557.40228, "b": 538.53487, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": ",", "bbox": {"l": 557.40399, "t": 532.01437, "r": 558.96893, "b": 538.5278900000001, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "COLING, pages 949-960. International Committee on Computational Linguistics,", "bbox": {"l": 333.39099, "t": 539.98438, "r": 558.9715, "b": 546.49789, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "dec 2020.", "bbox": {"l": 333.39099, "t": 547.95535, "r": 359.31955, "b": 554.46889, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "list_item", "bbox": {"l": 321.198, "t": 555.92535, "r": 558.90222, "b": 578.38586, "coord_origin": "TOPLEFT"}, "confidence": 0.9139776229858398, "cells": [{"id": 179, "text": "[8]", "bbox": {"l": 321.198, "t": 555.92535, "r": 329.7088, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction", "bbox": {"l": 331.51654, "t": 555.92535, "r": 558.19891, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "from pdf sources based on rule-based system using integrated formats. In", "bbox": {"l": 333.39099, "t": 563.89536, "r": 535.54352, "b": 570.40889, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "SemWe-", "bbox": {"l": 536.96399, "t": 563.93024, "r": 558.90222, "b": 570.4158600000001, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "bEval@ESWC", "bbox": {"l": 333.39099, "t": 571.9002399999999, "r": 371.94217, "b": 578.38586, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": ", 2016.", "bbox": {"l": 371.94299, "t": 571.86536, "r": 389.72617, "b": 578.3788900000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "list_item", "bbox": {"l": 321.198, "t": 579.83536, "r": 559.27448, "b": 610.2589, "coord_origin": "TOPLEFT"}, "confidence": 0.9113092422485352, "cells": [{"id": 185, "text": "[9]", "bbox": {"l": 321.198, "t": 579.83536, "r": 329.45999, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature", "bbox": {"l": 331.21487, "t": 579.83536, "r": 558.19843, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "hierarchies for accurate object detection and semantic segmentation. In", "bbox": {"l": 333.39099, "t": 587.8053600000001, "r": 543.05487, "b": 594.31889, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "IEEE", "bbox": {"l": 544.98499, "t": 587.84024, "r": 558.20148, "b": 594.32587, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Conference on Computer Vision and Pattern Recognition", "bbox": {"l": 333.39099, "t": 595.81024, "r": 491.61166, "b": 602.29587, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": ", CVPR, pages 580-587.", "bbox": {"l": 491.61301, "t": 595.77536, "r": 559.27448, "b": 602.28889, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "IEEE Computer Society, jun 2014.", "bbox": {"l": 333.39099, "t": 603.74536, "r": 428.59726000000006, "b": 610.2589, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574695229530334, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "list_item", "bbox": {"l": 317.95499, "t": 611.71536, "r": 558.20203, "b": 626.20686, "coord_origin": "TOPLEFT"}, "confidence": 0.9028682708740234, "cells": [{"id": 192, "text": "[10]", "bbox": {"l": 317.95499, "t": 611.71536, "r": 329.54767, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Ross B. Girshick. Fast R-CNN. In", "bbox": {"l": 331.31268, "t": 611.71536, "r": 425.1554, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "2015 IEEE International Conference on Computer", "bbox": {"l": 426.77802, "t": 611.75024, "r": 558.20203, "b": 618.23587, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Vision", "bbox": {"l": 333.39099, "t": 619.72124, "r": 350.59537, "b": 626.20686, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": ", ICCV, pages 1440-1448. IEEE Computer Society, dec 2015.", "bbox": {"l": 350.59598, "t": 619.68637, "r": 518.58777, "b": 626.19989, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859862327575684, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "list_item", "bbox": {"l": 317.95499, "t": 627.65637, "r": 558.20142, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9127580523490906, "cells": [{"id": 197, "text": "[11]", "bbox": {"l": 317.95499, "t": 627.65637, "r": 329.5459, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards", "bbox": {"l": 331.31064, "t": 627.65637, "r": 558.20142, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "real-time object detection with region proposal networks.", "bbox": {"l": 333.39099, "t": 635.62637, "r": 497.50909, "b": 642.13989, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "IEEE Transactions on", "bbox": {"l": 500.01401, "t": 635.66124, "r": 558.19885, "b": 642.14687, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Pattern Analysis and Machine Intelligence", "bbox": {"l": 333.39099, "t": 643.6312399999999, "r": 449.38620000000003, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": ", 39(6):1137-1149, 2017.", "bbox": {"l": 449.38699, "t": 643.59637, "r": 515.74268, "b": 650.10989, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 317.95499, "t": 651.56638, "r": 559.27808, "b": 674.01989, "coord_origin": "TOPLEFT"}, "confidence": 0.9142336249351501, "cells": [{"id": 203, "text": "[12]", "bbox": {"l": 317.95499, "t": 651.56638, "r": 329.41763, "b": 658.0799, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross B. Girshick. Mask R-CNN.", "bbox": {"l": 331.16287, "t": 651.56638, "r": 559.27808, "b": 658.0799, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "In", "bbox": {"l": 333.39099, "t": 659.53638, "r": 339.35904, "b": 666.0499, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "IEEE International Conference on Computer Vision", "bbox": {"l": 341.56299, "t": 659.57124, "r": 485.8273, "b": 666.05687, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ", ICCV, pages 2980-2988.", "bbox": {"l": 485.82901, "t": 659.53638, "r": 559.27356, "b": 666.0499, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "IEEE Computer Society, Oct 2017.", "bbox": {"l": 333.39099, "t": 667.50636, "r": 429.30161000000004, "b": 674.01989, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "list_item", "bbox": {"l": 317.95499, "t": 675.47636, "r": 558.97156, "b": 705.900894, "coord_origin": "TOPLEFT"}, "confidence": 0.8895393013954163, "cells": [{"id": 209, "text": "[13]", "bbox": {"l": 317.95499, "t": 675.47636, 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By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. 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Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. 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We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9676095843315125, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table", "bbox": {"l": 331.53516, "t": 348.70233, "r": 558.19904, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "competition. In", "bbox": {"l": 333.39099, "t": 356.67236, "r": 379.36414, "b": 363.18591, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "2013 12th International Conference on Document Analysis and", "bbox": {"l": 381.37201, "t": 356.70724, "r": 558.20099, "b": 363.19287, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Recognition", "bbox": {"l": 333.39099, "t": 364.67724999999996, "r": 365.59601, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ", pages 1449-1453, 2013.", "bbox": {"l": 365.59601, "t": 364.64236, "r": 434.29489, "b": 371.15591, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. 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In", "bbox": {"l": 333.39099, "t": 388.55236999999994, "r": 367.4339, "b": 395.06592, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "2017 14th IAPR International Conference on Document Analysis and", "bbox": {"l": 369.17401, "t": 388.58725000000004, "r": 558.20422, "b": 395.07288, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Recognition (ICDAR)", "bbox": {"l": 333.39099, "t": 396.55725, "r": 390.87601, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": ", volume 01, pages 1404-1410, 2017.", "bbox": {"l": 390.87698, "t": 396.52237, "r": 492.17831, "b": 403.03592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts rdcl2017. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) , volume 01, pages 1404-1410, 2017."}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 390.92053, "r": 294.04712, "b": 443.14014, "coord_origin": "TOPLEFT"}, "confidence": 0.9839397668838501, "cells": [{"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet\u2019s other labels as specified in table 3, and also PubLayNet\u2019s List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "list_item", "id": 17, "page_no": 7, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 321.198, "t": 404.49237, "r": 558.20013, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9257484078407288, "cells": [{"id": 148, "text": "[3]", "bbox": {"l": 321.198, "t": 404.49237, "r": 329.53583, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian", "bbox": {"l": 331.30682, "t": 404.49237, "r": 558.19958, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and", "bbox": {"l": 333.39099, "t": 412.46335, "r": 558.20013, "b": 418.9769, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "Recognition (cTDaR), April 2019. http://sac.founderit.com/.", "bbox": {"l": 333.39099, "t": 420.43335, "r": 501.80127, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[3] Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and Recognition (cTDaR), April 2019. http://sac.founderit.com/."}, {"label": "list_item", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "list_item", "bbox": {"l": 321.198, "t": 428.40335, "r": 559.37872, "b": 458.8269, "coord_origin": "TOPLEFT"}, "confidence": 0.9270963668823242, "cells": [{"id": 152, "text": "[4]", "bbox": {"l": 321.198, "t": 428.40335, "r": 329.91299, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on", "bbox": {"l": 331.7641, "t": 428.40335, "r": 558.19885, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "scientific literature parsing. In", "bbox": {"l": 333.39099, "t": 436.3733500000001, "r": 423.77225, "b": 442.8869, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Proceedings of the International Conference on", "bbox": {"l": 425.909, "t": 436.40823, "r": 558.20178, "b": 442.89386, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 444.37823, "r": 429.42697, "b": 450.86386, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": ", ICDAR, pages 605-617. LNCS 12824, Springer-", "bbox": {"l": 429.42400999999995, "t": 444.34335, "r": 559.37872, "b": 450.85689999999994, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Verlag, sep 2021.", "bbox": {"l": 332.819, "t": 452.31335, "r": 380.01764, "b": 458.8269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[4] Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on scientific literature parsing. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 605-617. LNCS 12824, SpringerVerlag, sep 2021."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "list_item", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 321.198, "t": 460.28336, "r": 559.02625, "b": 490.70792, "coord_origin": "TOPLEFT"}, "confidence": 0.9289959073066711, "cells": [{"id": 159, "text": "[5]", "bbox": {"l": 321.198, "t": 460.28336, "r": 329.22977, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin,", "bbox": {"l": 330.93576, "t": 460.28336, "r": 558.97156, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis:", "bbox": {"l": 333.39099, "t": 468.25336, "r": 559.02625, "b": 474.76691, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "not dead yet.", "bbox": {"l": 333.39099, "t": 476.22437, "r": 368.85431, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "International Journal on Document Analysis and Recognition (IJDAR)", "bbox": {"l": 370.811, "t": 476.25925, "r": 557.46326, "b": 482.74487, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": ",", "bbox": {"l": 557.46503, "t": 476.22437, "r": 558.96857, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "pages 1-11, 01 2022.", "bbox": {"l": 333.39099, "t": 484.19437, "r": 390.82715, "b": 490.70792, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[5] Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin, Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis: not dead yet. International Journal on Document Analysis and Recognition (IJDAR) , pages 1-11, 01 2022."}, {"label": "list_item", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 321.198, "t": 492.16437, "r": 558.20361, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.927467405796051, "cells": [{"id": 166, "text": "[6]", "bbox": {"l": 321.198, "t": 492.16437, "r": 329.42145, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset", "bbox": {"l": 331.16812, "t": 492.16437, "r": 558.20361, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "ever for document layout analysis. In", "bbox": {"l": 333.39099, "t": 500.13437, "r": 438.6101100000001, "b": 506.64792, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "Proceedings of the International Conference", "bbox": {"l": 440.349, "t": 500.16925, "r": 558.19958, "b": 506.65488, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "on Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 508.13925, "r": 441.40118, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": ", ICDAR, pages 1015-1022, sep 2019.", "bbox": {"l": 441.4019799999999, "t": 508.10437, "r": 544.78162, "b": 514.61792, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[6] Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset ever for document layout analysis. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 1015-1022, sep 2019."}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 321.19797, "t": 516.07437, "r": 558.9715, "b": 554.46889, "coord_origin": "TOPLEFT"}, "confidence": 0.9426887035369873, "cells": [{"id": 172, "text": "[7]", "bbox": {"l": 321.19797, "t": 516.07437, "r": 329.74161, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and", "bbox": {"l": 331.55634, "t": 516.07437, "r": 558.19897, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In", "bbox": {"l": 333.39099, "t": 524.0443700000001, "r": 558.19891, "b": 530.55792, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "Proceedings of the 28th International Conference on Computational Linguistics", "bbox": {"l": 333.39099, "t": 532.04922, "r": 557.40228, "b": 538.53487, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": ",", "bbox": {"l": 557.40399, "t": 532.01437, "r": 558.96893, "b": 538.5278900000001, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "COLING, pages 949-960. International Committee on Computational Linguistics,", "bbox": {"l": 333.39099, "t": 539.98438, "r": 558.9715, "b": 546.49789, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "dec 2020.", "bbox": {"l": 333.39099, "t": 547.95535, "r": 359.31955, "b": 554.46889, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[7] Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In Proceedings of the 28th International Conference on Computational Linguistics , COLING, pages 949-960. International Committee on Computational Linguistics, dec 2020."}, {"label": "list_item", "id": 19, "page_no": 7, "cluster": {"id": 19, "label": "list_item", "bbox": {"l": 321.198, "t": 555.92535, "r": 558.90222, "b": 578.38586, "coord_origin": "TOPLEFT"}, "confidence": 0.9139776229858398, "cells": [{"id": 179, "text": "[8]", "bbox": {"l": 321.198, "t": 555.92535, "r": 329.7088, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction", "bbox": {"l": 331.51654, "t": 555.92535, "r": 558.19891, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "from pdf sources based on rule-based system using integrated formats. In", "bbox": {"l": 333.39099, "t": 563.89536, "r": 535.54352, "b": 570.40889, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "SemWe-", "bbox": {"l": 536.96399, "t": 563.93024, "r": 558.90222, "b": 570.4158600000001, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "bEval@ESWC", "bbox": {"l": 333.39099, "t": 571.9002399999999, "r": 371.94217, "b": 578.38586, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": ", 2016.", "bbox": {"l": 371.94299, "t": 571.86536, "r": 389.72617, "b": 578.3788900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[8] Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction from pdf sources based on rule-based system using integrated formats. In SemWebEval@ESWC , 2016."}, {"label": "list_item", "id": 21, "page_no": 7, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 321.198, "t": 579.83536, "r": 559.27448, "b": 610.2589, "coord_origin": "TOPLEFT"}, "confidence": 0.9113092422485352, "cells": [{"id": 185, "text": "[9]", "bbox": {"l": 321.198, "t": 579.83536, "r": 329.45999, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature", "bbox": {"l": 331.21487, "t": 579.83536, "r": 558.19843, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "hierarchies for accurate object detection and semantic segmentation. In", "bbox": {"l": 333.39099, "t": 587.8053600000001, "r": 543.05487, "b": 594.31889, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "IEEE", "bbox": {"l": 544.98499, "t": 587.84024, "r": 558.20148, "b": 594.32587, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Conference on Computer Vision and Pattern Recognition", "bbox": {"l": 333.39099, "t": 595.81024, "r": 491.61166, "b": 602.29587, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": ", CVPR, pages 580-587.", "bbox": {"l": 491.61301, "t": 595.77536, "r": 559.27448, "b": 602.28889, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "IEEE Computer Society, jun 2014.", "bbox": {"l": 333.39099, "t": 603.74536, "r": 428.59726000000006, "b": 610.2589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[9] Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition , CVPR, pages 580-587. IEEE Computer Society, jun 2014."}, {"label": "section_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574695229530334, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example Predictions"}, {"label": "list_item", "id": 22, "page_no": 7, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 317.95499, "t": 611.71536, "r": 558.20203, "b": 626.20686, "coord_origin": "TOPLEFT"}, "confidence": 0.9028682708740234, "cells": [{"id": 192, "text": "[10]", "bbox": {"l": 317.95499, "t": 611.71536, "r": 329.54767, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Ross B. Girshick. Fast R-CNN. In", "bbox": {"l": 331.31268, "t": 611.71536, "r": 425.1554, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "2015 IEEE International Conference on Computer", "bbox": {"l": 426.77802, "t": 611.75024, "r": 558.20203, "b": 618.23587, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Vision", "bbox": {"l": 333.39099, "t": 619.72124, "r": 350.59537, "b": 626.20686, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": ", ICCV, pages 1440-1448. IEEE Computer Society, dec 2015.", "bbox": {"l": 350.59598, "t": 619.68637, "r": 518.58777, "b": 626.19989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[10] Ross B. Girshick. Fast R-CNN. In 2015 IEEE International Conference on Computer Vision , ICCV, pages 1440-1448. IEEE Computer Society, dec 2015."}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859862327575684, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "list_item", "id": 20, "page_no": 7, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 317.95499, "t": 627.65637, "r": 558.20142, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9127580523490906, "cells": [{"id": 197, "text": "[11]", "bbox": {"l": 317.95499, "t": 627.65637, "r": 329.5459, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards", "bbox": {"l": 331.31064, "t": 627.65637, "r": 558.20142, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "real-time object detection with region proposal networks.", "bbox": {"l": 333.39099, "t": 635.62637, "r": 497.50909, "b": 642.13989, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "IEEE Transactions on", "bbox": {"l": 500.01401, "t": 635.66124, "r": 558.19885, "b": 642.14687, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Pattern Analysis and Machine Intelligence", "bbox": {"l": 333.39099, "t": 643.6312399999999, "r": 449.38620000000003, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": ", 39(6):1137-1149, 2017.", "bbox": {"l": 449.38699, "t": 643.59637, "r": 515.74268, "b": 650.10989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[11] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence , 39(6):1137-1149, 2017."}, {"label": "list_item", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 317.95499, "t": 651.56638, "r": 559.27808, "b": 674.01989, "coord_origin": "TOPLEFT"}, "confidence": 0.9142336249351501, "cells": [{"id": 203, "text": "[12]", "bbox": {"l": 317.95499, "t": 651.56638, "r": 329.41763, "b": 658.0799, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross B. Girshick. Mask R-CNN.", "bbox": {"l": 331.16287, "t": 651.56638, "r": 559.27808, "b": 658.0799, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "In", "bbox": {"l": 333.39099, "t": 659.53638, "r": 339.35904, "b": 666.0499, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "IEEE International Conference on Computer Vision", "bbox": {"l": 341.56299, "t": 659.57124, "r": 485.8273, "b": 666.05687, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ", ICCV, pages 2980-2988.", "bbox": {"l": 485.82901, "t": 659.53638, "r": 559.27356, "b": 666.0499, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "IEEE Computer Society, Oct 2017.", "bbox": {"l": 333.39099, "t": 667.50636, "r": 429.30161000000004, "b": 674.01989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[12] Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross B. Girshick. Mask R-CNN. In IEEE International Conference on Computer Vision , ICCV, pages 2980-2988. IEEE Computer Society, Oct 2017."}, {"label": "list_item", "id": 23, "page_no": 7, "cluster": {"id": 23, "label": "list_item", "bbox": {"l": 317.95499, "t": 675.47636, "r": 558.97156, "b": 705.900894, "coord_origin": "TOPLEFT"}, "confidence": 0.8895393013954163, "cells": [{"id": 209, "text": "[13]", "bbox": {"l": 317.95499, "t": 675.47636, "r": 330.11407, "b": 681.9898900000001, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012,", "bbox": {"l": 331.96533, "t": 675.47636, "r": 558.96716, "b": 681.9898900000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "bbox": {"l": 333.18201, "t": 683.44637, "r": 558.96661, "b": 689.95989, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "bbox": {"l": 333.39099, "t": 691.41737, "r": 558.97156, "b": 697.930893, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "bbox": {"l": 333.39099, "t": 699.387367, "r": 558.20001, "b": 705.900894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu"}], "body": [{"label": "section_header", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319990873336792, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 CONCLUSION"}, {"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64868, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9824119210243225, "cells": [{"id": 2, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.32153, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 295.64868, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "cLayNet data-sets. By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"label": "table", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "table", "bbox": {"l": 72.65901184082031, "t": 172.4807891845703, "r": 274.8346862792969, "b": 339.85400390625, "coord_origin": "TOPLEFT"}, "confidence": 0.9892617464065552, "cells": [{"id": 8, "text": "Testing on", "bbox": {"l": 217.74099999999999, "t": 175.01855, "r": 256.26065, "b": 183.39319, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Training on", "bbox": {"l": 89.954002, "t": 185.97655999999995, "r": 133.24379, "b": 194.35119999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "labels", "bbox": {"l": 154.629, "t": 185.97655999999995, "r": 175.47588, "b": 194.35119999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "PLN", "bbox": {"l": 204.69, "t": 185.97655999999995, "r": 220.54260000000002, "b": 194.35119999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": 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We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9676095843315125, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table", "bbox": {"l": 331.53516, "t": 348.70233, "r": 558.19904, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "competition. In", "bbox": {"l": 333.39099, "t": 356.67236, "r": 379.36414, "b": 363.18591, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "2013 12th International Conference on Document Analysis and", "bbox": {"l": 381.37201, "t": 356.70724, "r": 558.20099, "b": 363.19287, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Recognition", "bbox": {"l": 333.39099, "t": 364.67724999999996, "r": 365.59601, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ", pages 1449-1453, 2013.", "bbox": {"l": 365.59601, "t": 364.64236, "r": 434.29489, "b": 371.15591, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. In 2013 12th International Conference on Document Analysis and Recognition , pages 1449-1453, 2013."}, {"label": "list_item", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 321.198, "t": 372.61237, "r": 559.37982, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9480941295623779, "cells": [{"id": 141, "text": "[2]", "bbox": {"l": 321.198, "t": 372.61237, "r": 329.85956, "b": 379.12592, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Ic-", "bbox": {"l": 331.69931, "t": 372.61237, "r": 559.37976, "b": 379.12592, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "dar2017 competition on recognition of documents with complex layouts -", "bbox": {"l": 333.39099, "t": 380.58237, "r": 559.37982, "b": 387.09592, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "rdcl2017. In", "bbox": {"l": 333.39099, "t": 388.55236999999994, "r": 367.4339, "b": 395.06592, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "2017 14th IAPR International Conference on Document Analysis and", "bbox": {"l": 369.17401, "t": 388.58725000000004, "r": 558.20422, "b": 395.07288, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Recognition (ICDAR)", "bbox": {"l": 333.39099, "t": 396.55725, "r": 390.87601, "b": 403.04287999999997, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": ", volume 01, pages 1404-1410, 2017.", "bbox": {"l": 390.87698, "t": 396.52237, "r": 492.17831, "b": 403.03592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts rdcl2017. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) , volume 01, pages 1404-1410, 2017."}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 390.92053, "r": 294.04712, "b": 443.14014, "coord_origin": "TOPLEFT"}, "confidence": 0.9839397668838501, "cells": [{"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet\u2019s other labels as specified in table 3, and also PubLayNet\u2019s List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "list_item", "id": 17, "page_no": 7, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 321.198, "t": 404.49237, "r": 558.20013, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9257484078407288, "cells": [{"id": 148, "text": "[3]", "bbox": {"l": 321.198, "t": 404.49237, "r": 329.53583, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian", "bbox": {"l": 331.30682, "t": 404.49237, "r": 558.19958, "b": 411.00592, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and", "bbox": {"l": 333.39099, "t": 412.46335, "r": 558.20013, "b": 418.9769, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "Recognition (cTDaR), April 2019. http://sac.founderit.com/.", "bbox": {"l": 333.39099, "t": 420.43335, "r": 501.80127, "b": 426.94689999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[3] Herv\u00e9 D\u00e9jean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and Recognition (cTDaR), April 2019. http://sac.founderit.com/."}, {"label": "list_item", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "list_item", "bbox": {"l": 321.198, "t": 428.40335, "r": 559.37872, "b": 458.8269, "coord_origin": "TOPLEFT"}, "confidence": 0.9270963668823242, "cells": [{"id": 152, "text": "[4]", "bbox": {"l": 321.198, "t": 428.40335, "r": 329.91299, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on", "bbox": {"l": 331.7641, "t": 428.40335, "r": 558.19885, "b": 434.9169, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "scientific literature parsing. In", "bbox": {"l": 333.39099, "t": 436.3733500000001, "r": 423.77225, "b": 442.8869, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Proceedings of the International Conference on", "bbox": {"l": 425.909, "t": 436.40823, "r": 558.20178, "b": 442.89386, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 444.37823, "r": 429.42697, "b": 450.86386, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": ", ICDAR, pages 605-617. LNCS 12824, Springer-", "bbox": {"l": 429.42400999999995, "t": 444.34335, "r": 559.37872, "b": 450.85689999999994, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Verlag, sep 2021.", "bbox": {"l": 332.819, "t": 452.31335, "r": 380.01764, "b": 458.8269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[4] Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on scientific literature parsing. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 605-617. LNCS 12824, SpringerVerlag, sep 2021."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "list_item", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 321.198, "t": 460.28336, "r": 559.02625, "b": 490.70792, "coord_origin": "TOPLEFT"}, "confidence": 0.9289959073066711, "cells": [{"id": 159, "text": "[5]", "bbox": {"l": 321.198, "t": 460.28336, "r": 329.22977, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin,", "bbox": {"l": 330.93576, "t": 460.28336, "r": 558.97156, "b": 466.79691, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis:", "bbox": {"l": 333.39099, "t": 468.25336, "r": 559.02625, "b": 474.76691, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "not dead yet.", "bbox": {"l": 333.39099, "t": 476.22437, "r": 368.85431, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "International Journal on Document Analysis and Recognition (IJDAR)", "bbox": {"l": 370.811, "t": 476.25925, "r": 557.46326, "b": 482.74487, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": ",", "bbox": {"l": 557.46503, "t": 476.22437, "r": 558.96857, "b": 482.73792, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "pages 1-11, 01 2022.", "bbox": {"l": 333.39099, "t": 484.19437, "r": 390.82715, "b": 490.70792, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[5] Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin, Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis: not dead yet. International Journal on Document Analysis and Recognition (IJDAR) , pages 1-11, 01 2022."}, {"label": "list_item", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 321.198, "t": 492.16437, "r": 558.20361, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.927467405796051, "cells": [{"id": 166, "text": "[6]", "bbox": {"l": 321.198, "t": 492.16437, "r": 329.42145, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset", "bbox": {"l": 331.16812, "t": 492.16437, "r": 558.20361, "b": 498.67792, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "ever for document layout analysis. In", "bbox": {"l": 333.39099, "t": 500.13437, "r": 438.6101100000001, "b": 506.64792, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "Proceedings of the International Conference", "bbox": {"l": 440.349, "t": 500.16925, "r": 558.19958, "b": 506.65488, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "on Document Analysis and Recognition", "bbox": {"l": 333.39099, "t": 508.13925, "r": 441.40118, "b": 514.6248800000001, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": ", ICDAR, pages 1015-1022, sep 2019.", "bbox": {"l": 441.4019799999999, "t": 508.10437, "r": 544.78162, "b": 514.61792, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[6] Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset ever for document layout analysis. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 1015-1022, sep 2019."}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 321.19797, "t": 516.07437, "r": 558.9715, "b": 554.46889, "coord_origin": "TOPLEFT"}, "confidence": 0.9426887035369873, "cells": [{"id": 172, "text": "[7]", "bbox": {"l": 321.19797, "t": 516.07437, "r": 329.74161, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and", "bbox": {"l": 331.55634, "t": 516.07437, "r": 558.19897, "b": 522.5879199999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In", "bbox": {"l": 333.39099, "t": 524.0443700000001, "r": 558.19891, "b": 530.55792, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "Proceedings of the 28th International Conference on Computational Linguistics", "bbox": {"l": 333.39099, "t": 532.04922, "r": 557.40228, "b": 538.53487, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": ",", "bbox": {"l": 557.40399, "t": 532.01437, "r": 558.96893, "b": 538.5278900000001, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "COLING, pages 949-960. International Committee on Computational Linguistics,", "bbox": {"l": 333.39099, "t": 539.98438, "r": 558.9715, "b": 546.49789, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "dec 2020.", "bbox": {"l": 333.39099, "t": 547.95535, "r": 359.31955, "b": 554.46889, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[7] Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In Proceedings of the 28th International Conference on Computational Linguistics , COLING, pages 949-960. International Committee on Computational Linguistics, dec 2020."}, {"label": "list_item", "id": 19, "page_no": 7, "cluster": {"id": 19, "label": "list_item", "bbox": {"l": 321.198, "t": 555.92535, "r": 558.90222, "b": 578.38586, "coord_origin": "TOPLEFT"}, "confidence": 0.9139776229858398, "cells": [{"id": 179, "text": "[8]", "bbox": {"l": 321.198, "t": 555.92535, "r": 329.7088, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction", "bbox": {"l": 331.51654, "t": 555.92535, "r": 558.19891, "b": 562.43889, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "from pdf sources based on rule-based system using integrated formats. In", "bbox": {"l": 333.39099, "t": 563.89536, "r": 535.54352, "b": 570.40889, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "SemWe-", "bbox": {"l": 536.96399, "t": 563.93024, "r": 558.90222, "b": 570.4158600000001, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "bEval@ESWC", "bbox": {"l": 333.39099, "t": 571.9002399999999, "r": 371.94217, "b": 578.38586, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": ", 2016.", "bbox": {"l": 371.94299, "t": 571.86536, "r": 389.72617, "b": 578.3788900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[8] Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction from pdf sources based on rule-based system using integrated formats. In SemWebEval@ESWC , 2016."}, {"label": "list_item", "id": 21, "page_no": 7, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 321.198, "t": 579.83536, "r": 559.27448, "b": 610.2589, "coord_origin": "TOPLEFT"}, "confidence": 0.9113092422485352, "cells": [{"id": 185, "text": "[9]", "bbox": {"l": 321.198, "t": 579.83536, "r": 329.45999, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature", "bbox": {"l": 331.21487, "t": 579.83536, "r": 558.19843, "b": 586.34889, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "hierarchies for accurate object detection and semantic segmentation. In", "bbox": {"l": 333.39099, "t": 587.8053600000001, "r": 543.05487, "b": 594.31889, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "IEEE", "bbox": {"l": 544.98499, "t": 587.84024, "r": 558.20148, "b": 594.32587, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Conference on Computer Vision and Pattern Recognition", "bbox": {"l": 333.39099, "t": 595.81024, "r": 491.61166, "b": 602.29587, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": ", CVPR, pages 580-587.", "bbox": {"l": 491.61301, "t": 595.77536, "r": 559.27448, "b": 602.28889, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "IEEE Computer Society, jun 2014.", "bbox": {"l": 333.39099, "t": 603.74536, "r": 428.59726000000006, "b": 610.2589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[9] Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition , CVPR, pages 580-587. IEEE Computer Society, jun 2014."}, {"label": "section_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574695229530334, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example Predictions"}, {"label": "list_item", "id": 22, "page_no": 7, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 317.95499, "t": 611.71536, "r": 558.20203, "b": 626.20686, "coord_origin": "TOPLEFT"}, "confidence": 0.9028682708740234, "cells": [{"id": 192, "text": "[10]", "bbox": {"l": 317.95499, "t": 611.71536, "r": 329.54767, "b": 618.2289000000001, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Ross B. Girshick. 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IEEE Computer Society, dec 2015."}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859862327575684, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "list_item", "id": 20, "page_no": 7, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 317.95499, "t": 627.65637, "r": 558.20142, "b": 650.1168700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9127580523490906, "cells": [{"id": 197, "text": "[11]", "bbox": {"l": 317.95499, "t": 627.65637, "r": 329.5459, "b": 634.16989, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 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IEEE Computer Society, Oct 2017."}, {"label": "list_item", "id": 23, "page_no": 7, "cluster": {"id": 23, "label": "list_item", "bbox": {"l": 317.95499, "t": 675.47636, "r": 558.97156, "b": 705.900894, "coord_origin": "TOPLEFT"}, "confidence": 0.8895393013954163, "cells": [{"id": 209, "text": "[13]", "bbox": {"l": 317.95499, "t": 675.47636, "r": 330.11407, "b": 681.9898900000001, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012,", "bbox": {"l": 331.96533, "t": 675.47636, "r": 558.96716, "b": 681.9898900000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "bbox": {"l": 333.18201, "t": 683.44637, "r": 558.96661, "b": 689.95989, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "bbox": {"l": 333.39099, "t": 691.41737, "r": 558.97156, "b": 697.930893, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "bbox": {"l": 333.39099, "t": 699.387367, "r": 558.20001, "b": 705.900894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu"}], "headers": [{"label": "page_header", "id": 24, "page_no": 7, "cluster": {"id": 24, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.79841548204422, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "4bed2a8aa51ac37058e79605821bbc426d032b0b6ca8bdf3409ed8508ccd8c67", "bbox": {"l": 231.8804, "t": 301.50543, "r": 235.14504999999997, "b": 414.69144, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "2f2a06d08f5ad565d0f5e815f4ddf666365b2cff435cdaeb8850217e8a8efabf", "bbox": {"l": 395.06876, "t": 117.37183000000005, "r": 398.33353, "b": 230.55786, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "7f2fd7293e04bf4f1756ae51f5779764933da1d1d2002e3915356050570fc75b", "bbox": {"l": 55.775887, "t": 301.50543, "r": 59.04052000000001, "b": 414.69144, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "1b81cf65f47456ad4faa725d1eb09879bd633af16cfe2bf8cea661b87907bfac", "bbox": {"l": 232.01364, "t": 117.37183000000005, "r": 235.27841000000004, "b": 230.55786, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "b60da9d26f488cb133e47d101d35fda1bdca2671ade60764d1cd569590270327", "bbox": {"l": 395.20047, "t": 301.50543, "r": 398.46512, "b": 414.69144, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "2b7b8355a42ebef0cf91583aad9f30f7c9fa63c5b05911730ba15275c024965b$^{A}$", "bbox": {"l": 55.775818, "t": 117.37183000000005, "r": 65.409912, "b": 230.55786, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "B", "bbox": {"l": 234.56980999999996, "t": 88.50183000000015, "r": 240.06987, "b": 97.01098999999988, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 397.81934, "t": 88.89355, "r": 403.3194, "b": 97.40270999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "D", "bbox": {"l": 59.909843, "t": 266.75885000000005, "r": 65.409912, "b": 275.26793999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "E", "bbox": {"l": 234.77386, "t": 266.36707, "r": 239.85495000000003, "b": 274.87616, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "F", "bbox": {"l": 398.26144, "t": 266.75885000000005, "r": 402.91592, "b": 275.26793999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Text", "bbox": {"l": 62.323874999999994, "t": 442.28543, "r": 70.895882, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Caption", "bbox": {"l": 80.16581, "t": 442.28543, "r": 95.565453, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "List-Item", "bbox": {"l": 104.94447, "t": 442.28543, "r": 122.38113000000001, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Formula", "bbox": {"l": 131.78354, "t": 442.28543, "r": 148.34625, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Table", "bbox": {"l": 157.66106, "t": 442.28543, "r": 168.53032, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Section-Header", "bbox": {"l": 201.24315, "t": 442.28543, "r": 232.00499, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Picture", "bbox": {"l": 177.8381, "t": 442.28543, "r": 191.88956, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Page-Header", "bbox": {"l": 240.95844000000002, "t": 442.28543, "r": 266.61908, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Page-Footer", "bbox": {"l": 276.03928, "t": 442.28543, "r": 300.33261, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Title", "bbox": {"l": 309.74615, "t": 442.28543, "r": 318.50473, "b": 448.26483, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on", "bbox": {"l": 53.79800000000001, "t": 464.48199, "r": 558.203, "b": 472.95523, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demon-", "bbox": {"l": 53.79800000000001, "t": 475.44101, "r": 559.80786, "b": 483.91425, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "strates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion", "bbox": {"l": 53.79800000000001, "t": 486.39999, "r": 558.20294, "b": 494.87323, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "and missing boxes.", "bbox": {"l": 53.79800000000001, "t": 497.358, "r": 130.37105, "b": 505.83124, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ul-", "bbox": {"l": 69.234001, "t": 527.06635, "r": 295.22406, "b": 533.5799, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow", "bbox": {"l": 69.234001, "t": 535.03638, "r": 294.30612, "b": 541.5499, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "export, opencv dnn support, October 2021.", "bbox": {"l": 69.234001, "t": 543.00638, "r": 190.45259, "b": 549.5199, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "[14]", "bbox": {"l": 53.79800000000001, "t": 550.97638, "r": 65.286942, "b": 557.4899, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander", "bbox": {"l": 67.036171, "t": 550.97638, "r": 294.17709, "b": 557.4899, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Kirillov, and Sergey Zagoruyko. 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(B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. 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Association for Computing Machinery."}], "headers": [{"label": "page_header", "id": 13, "page_no": 8, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8021655082702637, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8429455161094666, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}]
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.doctags.txt b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.doctags.txt
index 2aba1449..9352bf30 100644
--- a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.doctags.txt
+++ b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.doctags.txt
@@ -3,14 +3,14 @@
 <section_header_level_1><location><page_1><loc_22><loc_77><loc_52><loc_79></location>5.1 Hyper Parameter Optimization</section_header_level_1>
 <text><location><page_1><loc_22><loc_68><loc_79><loc_77></location>We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.</text>
 <table>
-<location><page_1><loc_24><loc_41><loc_77><loc_57></location>
+<location><page_1><loc_23><loc_41><loc_78><loc_57></location>
 <caption>Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.</caption>
 <row_0><col_0><col_header>#</col_0><col_1><col_header>#</col_1><col_2><col_header>Language</col_2><col_3><col_header>TEDs</col_3><col_4><col_header>TEDs</col_4><col_5><col_header>TEDs</col_5><col_6><col_header>mAP</col_6><col_7><col_header>Inference</col_7></row_0>
 <row_1><col_0><col_header>enc-layers</col_0><col_1><col_header>dec-layers</col_1><col_2><col_header>Language</col_2><col_3><col_header>simple</col_3><col_4><col_header>complex</col_4><col_5><col_header>all</col_5><col_6><col_header>(0.75)</col_6><col_7><col_header>time (secs)</col_7></row_1>
 <row_2><col_0><body>6</col_0><col_1><body>6</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.965 0.969</col_3><col_4><body>0.934 0.927</col_4><col_5><body>0.955 0.955</col_5><col_6><body>0.88 0.857</col_6><col_7><body>2.73 5.39</col_7></row_2>
-<row_3><col_0><body>4</col_0><col_1><body>4</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.938 0.952</col_3><col_4><body>0.904</col_4><col_5><body>0.927</col_5><col_6><body>0.853</col_6><col_7><body>1.97</col_7></row_3>
-<row_4><col_0><body>2</col_0><col_1><body>4</col_1><col_2><body>OTSL</col_2><col_3><body>0.923 0.945</col_3><col_4><body>0.909 0.897</col_4><col_5><body>0.938</col_5><col_6><body>0.843</col_6><col_7><body>3.77</col_7></row_4>
-<row_5><col_0><body></col_0><col_1><body></col_1><col_2><body>HTML</col_2><col_3><body></col_3><col_4><body>0.901</col_4><col_5><body>0.915 0.931</col_5><col_6><body>0.859 0.834</col_6><col_7><body>1.91 3.81</col_7></row_5>
+<row_3><col_0><body>4</col_0><col_1><body>4</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.938</col_3><col_4><body>0.904</col_4><col_5><body>0.927</col_5><col_6><body>0.853</col_6><col_7><body>1.97</col_7></row_3>
+<row_4><col_0><body></col_0><col_1><body></col_1><col_2><body>OTSL</col_2><col_3><body>0.952 0.923</col_3><col_4><body>0.909</col_4><col_5><body>0.938</col_5><col_6><body>0.843</col_6><col_7><body>3.77</col_7></row_4>
+<row_5><col_0><body>2</col_0><col_1><body>4</col_1><col_2><body>HTML</col_2><col_3><body>0.945</col_3><col_4><body>0.897 0.901</col_4><col_5><body>0.915 0.931</col_5><col_6><body>0.859 0.834</col_6><col_7><body>1.91 3.81</col_7></row_5>
 <row_6><col_0><body>4</col_0><col_1><body>2</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.952 0.944</col_3><col_4><body>0.92 0.903</col_4><col_5><body>0.942 0.931</col_5><col_6><body>0.857 0.824</col_6><col_7><body>1.22 2</col_7></row_6>
 </table>
 <section_header_level_1><location><page_1><loc_22><loc_35><loc_43><loc_36></location>5.2 Quantitative Results</section_header_level_1>
diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json
index 6f21d86b..8108fb67 100644
--- a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json
+++ b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json
@@ -1 +1 @@
-{"schema_name": "DoclingDocument", "version": "1.0.0", "name": "2305.03393v1-pg9", "origin": {"mimetype": "application/pdf", "binary_hash": 3463920545297462180, "filename": "2305.03393v1-pg9.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": "#/texts/1"}, {"cref": "#/texts/2"}, {"cref": "#/texts/3"}, {"cref": "#/texts/4"}, {"cref": "#/texts/5"}, {"cref": "#/tables/0"}, {"cref": "#/texts/6"}, {"cref": "#/texts/7"}, {"cref": "#/texts/8"}], "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 194.47799682617188, "t": 700.5064697265625, "r": 447.5447692871094, "b": 689.2177734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 475.9844055175781, "t": 700.5064697265625, "r": 480.5931396484375, "b": 689.2177734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9"}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 675.5369873046875, "r": 480.5966491699219, "b": 639.093017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 163]}], "orig": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 625.2948608398438, "r": 318.4514465332031, "b": 612.7918090820312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "level": 1}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 608.8849487304688, "r": 480.5956726074219, "b": 536.5759887695312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 423]}], "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 519.2052612304688, "r": 480.5989074707031, "b": 464.017822265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 398]}], "orig": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 286.3288879394531, "r": 264.4082946777344, "b": 273.8258056640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 269.9199523925781, "r": 480.72003173828125, "b": 173.6999969482422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 555]}], "orig": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 174.2779541015625, "r": 480.59857177734375, "b": 125.87999725341797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 289]}], "orig": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "pictures": [], "tables": [{"self_ref": "#/tables/0", "parent": {"cref": "#/body"}, "children": [], "label": "table", "prov": [{"page_no": 1, "bbox": {"l": 144.5919952392578, "t": 452.5425109863281, "r": 470.7695617675781, "b": 323.0968322753906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [{"cref": "#/texts/5"}], "references": [], "footnotes": [], "image": null, "data": {"table_cells": [{"bbox": {"l": 160.3699951171875, "t": 452.5425109863281, "r": 168.04522705078125, "b": 441.2538146972656, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "#", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 207.9739990234375, "t": 452.5425109863281, "r": 215.64923095703125, "b": 441.2538146972656, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "#", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 239.79800415039062, "t": 447.0635070800781, "r": 278.33380126953125, "b": 435.7748107910156, "coord_origin": "BOTTOMLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Language", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.6700134277344, "t": 452.5425109863281, "r": 348.2641906738281, "b": 441.2538146972656, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 396.27099609375, "t": 452.5425109863281, "r": 417.1259460449219, "b": 441.2538146972656, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "mAP", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 430.77099609375, "t": 452.5425109863281, "r": 467.14141845703125, "b": 441.2538146972656, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "Inference", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 144.5919952392578, "t": 439.5915222167969, "r": 183.82894897460938, "b": 428.3028259277344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "enc-layers", "column_header": true, 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4, "end_col_offset_idx": 5, "text": "complex", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 369.3059997558594, "t": 439.5915222167969, "r": 379.0291442871094, "b": 428.3028259277344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "all", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 394.927001953125, "t": 441.5835266113281, "r": 418.4692077636719, "b": 430.2948303222656, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "(0.75)", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 427.14801025390625, "t": 441.5835266113281, "r": 470.7695617675781, "b": 430.2948303222656, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "time (secs)", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 161.906005859375, "t": 420.7615051269531, "r": 166.51473999023438, "b": 409.4728088378906, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "6", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.50900268554688, "t": 420.7615051269531, "r": 214.11773681640625, "b": 409.4728088378906, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "6", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.17599487304688, "t": 426.24151611328125, "r": 272.9449462890625, "b": 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Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 625.2948608398438, "r": 318.4514465332031, "b": 612.7918090820312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "level": 1}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 608.8849487304688, "r": 480.5956726074219, "b": 536.5759887695312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 423]}], "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 519.2052612304688, "r": 480.5989074707031, "b": 464.017822265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 398]}], "orig": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 286.3288879394531, "r": 264.4082946777344, "b": 273.8258056640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 269.9199523925781, "r": 480.72003173828125, "b": 173.6999969482422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 555]}], "orig": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 174.2779541015625, "r": 480.59857177734375, "b": 125.87999725341797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 289]}], "orig": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "pictures": [], "tables": [{"self_ref": "#/tables/0", "parent": {"cref": "#/body"}, "children": [], "label": "table", "prov": [{"page_no": 1, "bbox": {"l": 139.6674041748047, "t": 454.4546203613281, "r": 475.00927734375, "b": 322.5054626464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [{"cref": "#/texts/5"}], "references": [], "footnotes": [], "image": null, "data": {"table_cells": [{"bbox": {"l": 160.3699951171875, "t": 452.5425109863281, "r": 168.04522705078125, "b": 441.2538146972656, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "#", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 207.9739990234375, "t": 452.5425109863281, "r": 215.64923095703125, "b": 441.2538146972656, 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false, "row_section": false}, {"bbox": {"l": 439.5270080566406, "t": 373.7012939453125, "r": 458.38336181640625, "b": 349.3988342285156, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.91 3.81", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 161.906005859375, "t": 341.8575134277344, "r": 166.51473999023438, "b": 330.5688171386719, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.50900268554688, "t": 341.8575134277344, "r": 214.11773681640625, "b": 330.5688171386719, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, 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458.38336181640625, "b": 323.0968322753906, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}]]}}], "key_value_items": [], "pages": {"1": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 1}}}
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.md b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.md
index 27e932bc..45466f7d 100644
--- a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.md
+++ b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.md
@@ -10,9 +10,9 @@ Table 1. HPO performed in OTSL and HTML representation on the same transformer-b
 |------------|------------|------------|-------------|-------------|-------------|-------------|-------------|
 | enc-layers | dec-layers | Language   | simple      | complex     | all         | (0.75)      | time (secs) |
 | 6          | 6          | OTSL HTML  | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857  | 2.73 5.39   |
-| 4          | 4          | OTSL HTML  | 0.938 0.952 | 0.904       | 0.927       | 0.853       | 1.97        |
-| 2          | 4          | OTSL       | 0.923 0.945 | 0.909 0.897 | 0.938       | 0.843       | 3.77        |
-|            |            | HTML       |             | 0.901       | 0.915 0.931 | 0.859 0.834 | 1.91 3.81   |
+| 4          | 4          | OTSL HTML  | 0.938       | 0.904       | 0.927       | 0.853       | 1.97        |
+|            |            | OTSL       | 0.952 0.923 | 0.909       | 0.938       | 0.843       | 3.77        |
+| 2          | 4          | HTML       | 0.945       | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81   |
 | 4          | 2          | OTSL HTML  | 0.952 0.944 | 0.92 0.903  | 0.942 0.931 | 0.857 0.824 | 1.22 2      |
 
 ## 5.2 Quantitative Results
diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json
index 363a49b3..c25f672e 100644
--- a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json
+++ b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Language", "bbox": {"l": 239.79799999999997, "t": 344.93649, "r": 278.3338, "b": 356.22519000000005, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TEDs", "bbox": {"l": 324.67001, "t": 339.45749, "r": 348.26419, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "mAP", "bbox": {"l": 396.271, "t": 339.45749, "r": 417.12595, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "(0.75)", "bbox": {"l": 394.927, "t": 350.41647, "r": 418.46921, "b": 361.70517, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Inference", "bbox": {"l": 430.771, "t": 339.45749, "r": 467.14142000000004, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "time (secs)", "bbox": {"l": 427.14801, "t": 350.41647, "r": 470.76955999999996, "b": 361.70517, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "simple", "bbox": {"l": 286.686, "t": 352.40848, "r": 312.32812, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "complex", "bbox": {"l": 320.702, "t": 352.40848, "r": 353.71539, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "all", "bbox": {"l": 369.306, "t": 352.40848, "r": 379.02914, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "6", "bbox": {"l": 161.90601, "t": 371.23849, "r": 166.51474, "b": 382.52719, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "6", "bbox": {"l": 209.509, "t": 371.23849, "r": 214.11774, "b": 382.52719, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 365.75848, "r": 271.41064, "b": 377.04717999999997, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "0.965", "bbox": {"l": 289.017, "t": 365.75848, "r": 310.00732, "b": 377.04717999999997, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "0.934", "bbox": {"l": 326.71701, "t": 365.75848, "r": 347.70734, "b": 377.04717999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "0.955", "bbox": {"l": 363.67599, "t": 365.75848, "r": 384.66632, "b": 377.04717999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "0.88", "bbox": {"l": 397.26999, "t": 365.69571, "r": 416.12634, "b": 377.10098000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "2.73", "bbox": {"l": 439.52701, "t": 365.69571, "r": 458.38336, "b": 377.10098000000005, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 378.71048, "r": 272.94495, "b": 389.99917999999997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "0.969", "bbox": {"l": 289.017, "t": 378.71048, "r": 310.00732, "b": 389.99917999999997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "0.927", "bbox": {"l": 326.71701, "t": 378.71048, "r": 347.70734, "b": 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"0.904", "bbox": {"l": 326.71701, "t": 392.06049, "r": 347.70734, "b": 403.34918, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "0.927", "bbox": {"l": 363.67599, "t": 392.06049, "r": 384.66632, "b": 403.34918, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "0.853", "bbox": {"l": 394.61801, "t": 391.99771, "r": 418.77798, "b": 403.40298, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1.97", "bbox": {"l": 439.52701, "t": 391.99771, "r": 458.38336, "b": 403.40298, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 405.01147, "r": 272.94495, "b": 416.30017, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "0.952", "bbox": {"l": 289.017, "t": 405.01147, "r": 310.00732, "b": 416.30017, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "0.909", "bbox": {"l": 326.71701, "t": 405.01147, "r": 347.70734, "b": 416.30017, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "0.938", "bbox": {"l": 362.08801, "t": 404.9486999999999, "r": 386.24799, "b": 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{"id": 83, "text": "0.857", "bbox": {"l": 394.61801, "t": 444.5996999999999, "r": 418.77798, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1.22", "bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 457.61447, "r": 272.94495, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "0.944", "bbox": {"l": 289.017, "t": 457.61447, "r": 310.00732, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0.903", "bbox": {"l": 326.71701, "t": 457.61447, "r": 347.70734, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "0.931", "bbox": {"l": 363.67599, "t": 457.61447, "r": 384.66632, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0.824", "bbox": {"l": 396.20599, "t": 457.61447, "r": 417.19632, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "2", "bbox": {"l": 446.65302, "t": 457.61447, "r": 451.26175, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373531937599182, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.957740306854248, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591906070709229, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 144.592, "t": 339.45749, "r": 470.76955999999996, "b": 468.90317, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Language", "bbox": {"l": 239.79799999999997, "t": 344.93649, "r": 278.3338, "b": 356.22519000000005, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TEDs", "bbox": {"l": 324.67001, "t": 339.45749, "r": 348.26419, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "mAP", "bbox": {"l": 396.271, "t": 339.45749, "r": 417.12595, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "(0.75)", "bbox": {"l": 394.927, "t": 350.41647, "r": 418.46921, "b": 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[{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.957740306854248, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591906070709229, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 144.592, "t": 339.45749, "r": 470.76955999999996, "b": 468.90317, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 363.69717, "coord_origin": 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[]}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849976301193237, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.957740306854248, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591906070709229, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 144.592, "t": 339.45749, "r": 470.76955999999996, "b": 468.90317, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 363.69717, "coord_origin": 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6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 450.14248999999995, "r": 214.11774, "b": 461.43118, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.17598999999998, "t": 444.66248, "r": 272.94495, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 444.66248, "r": 310.00732, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, 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"start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.857 0.824", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589295387268066, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849976301193237, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373531937599182, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9"}]}}]
\ No newline at end of file
+[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 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{"id": 83, "text": "0.857", "bbox": {"l": 394.61801, "t": 444.5996999999999, "r": 418.77798, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1.22", "bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 457.61447, "r": 272.94495, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "0.944", "bbox": {"l": 289.017, "t": 457.61447, "r": 310.00732, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0.903", "bbox": {"l": 326.71701, "t": 457.61447, "r": 347.70734, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "0.931", "bbox": {"l": 363.67599, "t": 457.61447, "r": 384.66632, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0.824", "bbox": {"l": 396.20599, "t": 457.61447, "r": 417.19632, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "2", "bbox": {"l": 446.65302, "t": 457.61447, "r": 451.26175, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373531937599182, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.957740306854248, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591906070709229, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Language", "bbox": {"l": 239.79799999999997, "t": 344.93649, "r": 278.3338, "b": 356.22519000000005, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TEDs", "bbox": {"l": 324.67001, "t": 339.45749, "r": 348.26419, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "mAP", "bbox": {"l": 396.271, "t": 339.45749, "r": 417.12595, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "(0.75)", "bbox": {"l": 394.927, "t": 350.41647, 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Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.957740306854248, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591906070709229, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 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480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.957740306854248, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591906070709229, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 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"col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 444.66248, "r": 310.00732, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.952 0.944", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.71701, "t": 444.66248, "r": 347.70734, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.92 0.903", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 362.08801, "t": 444.5996999999999, "r": 386.24799, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.942 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.61801, "t": 444.5996999999999, "r": 418.77798, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.857 0.824", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589295387268066, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849976301193237, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373531937599182, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9"}]}}]
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1.doctags.txt b/tests/data/groundtruth/docling_v2/2305.03393v1.doctags.txt
index 1dece76e..c4e28f06 100644
--- a/tests/data/groundtruth/docling_v2/2305.03393v1.doctags.txt
+++ b/tests/data/groundtruth/docling_v2/2305.03393v1.doctags.txt
@@ -80,30 +80,30 @@
 <section_header_level_1><location><page_9><loc_22><loc_78><loc_52><loc_79></location>5.1 Hyper Parameter Optimization</section_header_level_1>
 <text><location><page_9><loc_22><loc_68><loc_79><loc_77></location>We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.</text>
 <table>
-<location><page_9><loc_24><loc_41><loc_77><loc_57></location>
+<location><page_9><loc_23><loc_41><loc_78><loc_57></location>
 <caption>Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.</caption>
 <row_0><col_0><col_header>#</col_0><col_1><col_header>#</col_1><col_2><col_header>Language</col_2><col_3><col_header>TEDs</col_3><col_4><col_header>TEDs</col_4><col_5><col_header>TEDs</col_5><col_6><col_header>mAP</col_6><col_7><col_header>Inference</col_7></row_0>
-<row_1><col_0><col_header>enc-layers</col_0><col_1><col_header>dec-layers</col_1><col_2><body></col_2><col_3><col_header>simple</col_3><col_4><col_header>complex</col_4><col_5><col_header>all</col_5><col_6><col_header>(0.75)</col_6><col_7><col_header>time (secs)</col_7></row_1>
+<row_1><col_0><col_header>enc-layers</col_0><col_1><col_header>dec-layers</col_1><col_2><col_header>Language</col_2><col_3><col_header>simple</col_3><col_4><col_header>complex</col_4><col_5><col_header>all</col_5><col_6><col_header>(0.75)</col_6><col_7><col_header>time (secs)</col_7></row_1>
 <row_2><col_0><body>6</col_0><col_1><body>6</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.965 0.969</col_3><col_4><body>0.934 0.927</col_4><col_5><body>0.955 0.955</col_5><col_6><body>0.88 0.857</col_6><col_7><body>2.73 5.39</col_7></row_2>
-<row_3><col_0><body>4</col_0><col_1><body>4</col_1><col_2><body>OTSL</col_2><col_3><body>0.938</col_3><col_4><body>0.904</col_4><col_5><body>0.927</col_5><col_6><body>0.853</col_6><col_7><body>1.97</col_7></row_3>
-<row_4><col_0><body></col_0><col_1><body></col_1><col_2><body>HTML</col_2><col_3><body>0.952</col_3><col_4><body>0.909</col_4><col_5><body>0.938</col_5><col_6><body>0.843</col_6><col_7><body>3.77</col_7></row_4>
-<row_5><col_0><body>2</col_0><col_1><body>4</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.923 0.945</col_3><col_4><body>0.897 0.901</col_4><col_5><body>0.915 0.931</col_5><col_6><body>0.859 0.834</col_6><col_7><body>1.91</col_7></row_5>
-<row_6><col_0><body>4</col_0><col_1><body>2</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.952 0.944</col_3><col_4><body>0.92 0.903</col_4><col_5><body>0.942 0.931</col_5><col_6><body>0.857 0.824</col_6><col_7><body>3.81 1.22 2</col_7></row_6>
+<row_3><col_0><body>4</col_0><col_1><body>4</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.938 0.952</col_3><col_4><body>0.904</col_4><col_5><body>0.927</col_5><col_6><body>0.853</col_6><col_7><body>1.97</col_7></row_3>
+<row_4><col_0><body>2</col_0><col_1><body>4</col_1><col_2><body>OTSL</col_2><col_3><body>0.923 0.945</col_3><col_4><body>0.909 0.897</col_4><col_5><body>0.938</col_5><col_6><body>0.843</col_6><col_7><body>3.77</col_7></row_4>
+<row_5><col_0><body></col_0><col_1><body></col_1><col_2><body>HTML</col_2><col_3><body></col_3><col_4><body>0.901</col_4><col_5><body>0.915 0.931</col_5><col_6><body>0.859 0.834</col_6><col_7><body>1.91 3.81</col_7></row_5>
+<row_6><col_0><body>4</col_0><col_1><body>2</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.952 0.944</col_3><col_4><body>0.92 0.903</col_4><col_5><body>0.942 0.931</col_5><col_6><body>0.857 0.824</col_6><col_7><body>1.22 2</col_7></row_6>
 </table>
 <section_header_level_1><location><page_9><loc_22><loc_35><loc_43><loc_36></location>5.2 Quantitative Results</section_header_level_1>
 <text><location><page_9><loc_22><loc_22><loc_79><loc_34></location>We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.</text>
 <text><location><page_9><loc_22><loc_16><loc_79><loc_22></location>Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.</text>
 <table>
-<location><page_10><loc_24><loc_67><loc_76><loc_80></location>
+<location><page_10><loc_23><loc_67><loc_77><loc_80></location>
 <caption>Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).</caption>
-<row_0><col_0><body></col_0><col_1><col_header>Language</col_1><col_2><col_header>TEDs</col_2><col_3><col_header>TEDs</col_3><col_4><col_header>TEDs</col_4><col_5><col_header>mAP(0.75)</col_5><col_6><col_header>Inference</col_6></row_0>
-<row_1><col_0><col_header>Data set</col_0><col_1><body></col_1><col_2><col_header>simple</col_2><col_3><col_header>complex</col_3><col_4><col_header>all</col_4><col_5><body></col_5><col_6><col_header>time (secs)</col_6></row_1>
-<row_2><col_0><row_header>PubTabNet</col_0><col_1><body>OTSL</col_1><col_2><body>0.965</col_2><col_3><body>0.934</col_3><col_4><body>0.955</col_4><col_5><body>0.88</col_5><col_6><body>2.73</col_6></row_2>
-<row_3><col_0><row_header>PubTabNet</col_0><col_1><body>HTML</col_1><col_2><body>0.969</col_2><col_3><body>0.927</col_3><col_4><body>0.955</col_4><col_5><body>0.857</col_5><col_6><body>5.39</col_6></row_3>
-<row_4><col_0><row_header>FinTabNet</col_0><col_1><body>OTSL</col_1><col_2><body>0.955</col_2><col_3><body>0.961</col_3><col_4><body>0.959</col_4><col_5><body>0.862</col_5><col_6><body>1.85</col_6></row_4>
-<row_5><col_0><row_header>FinTabNet</col_0><col_1><body>HTML</col_1><col_2><body>0.917</col_2><col_3><body>0.922</col_3><col_4><body>0.92</col_4><col_5><body>0.722</col_5><col_6><body>3.26</col_6></row_5>
-<row_6><col_0><row_header>PubTables-1M</col_0><col_1><body>OTSL</col_1><col_2><body>0.987</col_2><col_3><body>0.964</col_3><col_4><body>0.977</col_4><col_5><body>0.896</col_5><col_6><body>1.79</col_6></row_6>
-<row_7><col_0><row_header>PubTables-1M</col_0><col_1><body>HTML</col_1><col_2><body>0.983</col_2><col_3><body>0.944</col_3><col_4><body>0.966</col_4><col_5><body>0.889</col_5><col_6><body>3.26</col_6></row_7>
+<row_0><col_0><body></col_0><col_1><col_header>Language</col_1><col_2><col_header>TEDs</col_2><col_3><col_header>TEDs</col_3><col_4><col_header>TEDs</col_4><col_5><col_header>mAP(0.75)</col_5><col_6><col_header>Inference time (secs)</col_6></row_0>
+<row_1><col_0><body></col_0><col_1><col_header>Language</col_1><col_2><col_header>simple</col_2><col_3><col_header>complex</col_3><col_4><col_header>all</col_4><col_5><col_header>mAP(0.75)</col_5><col_6><col_header>Inference time (secs)</col_6></row_1>
+<row_2><col_0><row_header>PubTabNet</col_0><col_1><row_header>OTSL</col_1><col_2><body>0.965</col_2><col_3><body>0.934</col_3><col_4><body>0.955</col_4><col_5><body>0.88</col_5><col_6><body>2.73</col_6></row_2>
+<row_3><col_0><row_header>PubTabNet</col_0><col_1><row_header>HTML</col_1><col_2><body>0.969</col_2><col_3><body>0.927</col_3><col_4><body>0.955</col_4><col_5><body>0.857</col_5><col_6><body>5.39</col_6></row_3>
+<row_4><col_0><row_header>FinTabNet</col_0><col_1><row_header>OTSL</col_1><col_2><body>0.955</col_2><col_3><body>0.961</col_3><col_4><body>0.959</col_4><col_5><body>0.862</col_5><col_6><body>1.85</col_6></row_4>
+<row_5><col_0><row_header>FinTabNet</col_0><col_1><row_header>HTML</col_1><col_2><body>0.917</col_2><col_3><body>0.922</col_3><col_4><body>0.92</col_4><col_5><body>0.722</col_5><col_6><body>3.26</col_6></row_5>
+<row_6><col_0><row_header>PubTables-1M</col_0><col_1><row_header>OTSL</col_1><col_2><body>0.987</col_2><col_3><body>0.964</col_3><col_4><body>0.977</col_4><col_5><body>0.896</col_5><col_6><body>1.79</col_6></row_6>
+<row_7><col_0><row_header>PubTables-1M</col_0><col_1><row_header>HTML</col_1><col_2><body>0.983</col_2><col_3><body>0.944</col_3><col_4><body>0.966</col_4><col_5><body>0.889</col_5><col_6><body>3.26</col_6></row_7>
 </table>
 <section_header_level_1><location><page_10><loc_22><loc_62><loc_42><loc_64></location>5.3 Qualitative Results</section_header_level_1>
 <text><location><page_10><loc_22><loc_54><loc_79><loc_61></location>To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes.</text>
diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1.json b/tests/data/groundtruth/docling_v2/2305.03393v1.json
index f261e9f7..6edc18d7 100644
--- a/tests/data/groundtruth/docling_v2/2305.03393v1.json
+++ b/tests/data/groundtruth/docling_v2/2305.03393v1.json
@@ -1 +1 @@
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"BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023"}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 676.1008911132812, "r": 480.59735107421875, "b": 645.4859008789062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition", "level": 1}, {"self_ref": "#/texts/2", "parent": {"cref": "#/groups/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 139.34305, "t": 622.30841, "r": 476.01270000000005, "b": 587.61926, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 238]}], "orig": "Maksym Lysak [0000 \u2212 0002 \u2212 3723 \u2212 $^{6960]}$, Ahmed Nassar[0000 \u2212 0002 \u2212 9468 \u2212 $^{0822]}$, Nikolaos Livathinos [0000 \u2212 0001 \u2212 8513 \u2212 $^{3491]}$, Christoph Auer[0000 \u2212 0001 \u2212 5761 \u2212 $^{0422]}$, and Peter Staar [0000 \u2212 0002 \u2212 8088 \u2212 0823]", "text": "Maksym Lysak [0000 \u2212 0002 \u2212 3723 \u2212 $^{6960]}$, Ahmed Nassar[0000 \u2212 0002 \u2212 9468 \u2212 $^{0822]}$, Nikolaos Livathinos [0000 \u2212 0001 \u2212 8513 \u2212 $^{3491]}$, Christoph Auer[0000 \u2212 0001 \u2212 5761 \u2212 $^{0422]}$, and Peter Staar [0000 \u2212 0002 \u2212 8088 \u2212 0823]"}, {"self_ref": "#/texts/3", "parent": {"cref": "#/groups/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 279.1051, "t": 574.79602, "r": 336.25153, "b": 566.72632, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "IBM Research", "text": "IBM Research"}, {"self_ref": "#/texts/4", "parent": {"cref": "#/groups/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 222.96609, "t": 563.19147, "r": 392.38983, "b": 555.72247, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "{mly,ahn,nli,cau,taa}@zurich.ibm.com", "text": "{mly,ahn,nli,cau,taa}@zurich.ibm.com"}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 163.11109924316406, "t": 521.6988525390625, "r": 452.248779296875, "b": 327.2655334472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1198]}], "orig": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community.", "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 163.11109924316406, "t": 313.3060607910156, "r": 452.2415771484375, "b": 294.2145080566406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization.", "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 269.88031005859375, "r": 228.933837890625, "b": 259.3119201660156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "1 Introduction", "text": "1 Introduction", "level": 1}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 243.7134552001953, "r": 480.595947265625, "b": 163.18548583984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 500]}], "orig": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods.", "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 159.85244750976562, "r": 480.5958251953125, "b": 127.14546966552734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 235]}], "orig": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of", "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of"}, {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 665.6658325195312, "r": 480.5918884277344, "b": 591.7794189453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 574]}], "orig": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).", "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"self_ref": "#/texts/13", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 154.3298, "t": 578.42542, "r": 159.79837, "b": 571.3208, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/14", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 321.07053, "t": 578.42542, "r": 326.53909, "b": 571.3208, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/15", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 384.11816, "t": 533.45282, "r": 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11]}], "orig": "rowspan=\"2\"", "text": "rowspan=\"2\""}, {"self_ref": "#/texts/116", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 194.52863, "t": 373.89478, "r": 232.90777999999997, "b": 367.50064, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "rowspan=\"3\"", "text": "rowspan=\"3\""}, {"self_ref": "#/texts/117", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 236.69518999999997, "t": 373.89478, "r": 240.67617999999996, "b": 367.50064, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ">", "text": ">"}, {"self_ref": "#/texts/118", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 244.46358, "t": 373.89478, "r": 269.10144, "b": 367.50064, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "</table>", "text": "</table>"}, {"self_ref": "#/texts/119", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 339.68621826171875, "r": 480.5923156738281, "b": 271.1133117675781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 435]}], "orig": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"self_ref": "#/texts/120", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 134.7650146484375, "t": 267.44927978515625, "r": 480.5948181152344, "b": 127.14530181884766, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 911]}], "orig": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR.", "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}, {"self_ref": "#/texts/121", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/122", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 673.0662231445312, "r": 480.5918273925781, "b": 580.5831298828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 584]}], "orig": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments.", "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"self_ref": "#/texts/124", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 577.1641235351562, "r": 480.5957336425781, "b": 460.7701416015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 721]}], "orig": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML.", "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"self_ref": "#/texts/125", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 457.35211181640625, "r": 480.5956726074219, "b": 352.9132385253906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 626]}], "orig": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps.", "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"self_ref": "#/texts/126", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 329.91204833984375, "r": 236.76913452148438, "b": 319.3436584472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "2 Related Work", "text": "2 Related Work", "level": 1}, {"self_ref": "#/texts/127", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 303.3141784667969, "r": 484.1204833984375, "b": 127.14423370361328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1161]}], "orig": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell.", "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}, {"self_ref": "#/texts/128", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/129", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/130", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.59576416015625, "b": 532.7620849609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 939]}], "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"self_ref": "#/texts/131", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 529.3430786132812, "r": 480.595703125, "b": 305.3533020019531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1404]}], "orig": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content.", "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"self_ref": "#/texts/132", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 301.93426513671875, "r": 480.5937805175781, "b": 209.4513397216797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 572]}], "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task.", "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"self_ref": "#/texts/133", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 186.45016479492188, "r": 269.6244201660156, "b": 175.88177490234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "3 Problem Statement", "text": "3 Problem Statement", "level": 1}, {"self_ref": "#/texts/134", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 159.85231018066406, "r": 480.59368896484375, "b": 127.14434051513672, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 233]}], "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-", "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}, {"self_ref": "#/texts/135", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/136", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/137", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5937805175781, "b": 604.4931640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 422]}], "orig": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary.", "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"self_ref": "#/texts/138", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 145.6070098876953, "t": 570.9207153320312, "r": 469.7522277832031, "b": 562.7882080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"self_ref": "#/texts/139", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.76499938964844, "t": 423.793212890625, "r": 480.5947570800781, "b": 259.57940673828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1021]}], "orig": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure.", "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"self_ref": "#/texts/140", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.7650146484375, "t": 255.95736694335938, "r": 480.5928955078125, "b": 211.29440307617188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 313]}], "orig": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible.", "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"self_ref": "#/texts/141", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.7650146484375, "t": 207.67337036132812, "r": 480.5947265625, "b": 127.14539337158203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 542]}], "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence", "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"self_ref": "#/texts/142", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6"}, {"self_ref": "#/texts/143", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/144", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.59478759765625, "b": 652.314208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 132]}], "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output.", "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"self_ref": "#/texts/145", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 648.5172119140625, "r": 480.595703125, "b": 496.2580871582031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 977]}], "orig": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content.", "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"self_ref": "#/texts/146", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 471.368896484375, "r": 372.50848388671875, "b": 460.8005065917969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "4 Optimised Table Structure Language", "text": "4 Optimised Table Structure Language", "level": 1}, {"self_ref": "#/texts/147", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 442.8830261230469, "r": 480.5947265625, "b": 350.400146484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 563]}], "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture.", "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"self_ref": "#/texts/148", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 326.1280822753906, "r": 261.80108642578125, "b": 317.3211364746094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "4.1 Language Definition", "text": "4.1 Language Definition", "level": 1}, {"self_ref": "#/texts/149", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 303.0021057128906, "r": 480.5887145996094, "b": 270.2941589355469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 165]}], "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid.", "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"self_ref": "#/texts/150", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 149.708984375, "t": 266.4981384277344, "r": 409.3113708496094, "b": 257.701171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "The OTSL vocabulary is comprised of the following tokens:", "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"self_ref": "#/texts/151", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.99298095703125, "t": 244.0301055908203, "r": 460.54443359375, "b": 235.22317504882812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "-\"C\" cell a new table cell that either has or does not have cell content", "text": "-\"C\" cell a new table cell that either has or does not have cell content", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/152", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.99301147460938, "t": 231.43710327148438, "r": 480.59393310546875, "b": 210.6751708984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/153", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 206.8881072998047, "r": 480.58856201171875, "b": 186.1261749267578, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/154", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 182.34010314941406, "r": 454.5549621582031, "b": 173.53317260742188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells", "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/155", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 169.74610900878906, "r": 328.61676025390625, "b": 160.93917846679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "-\"NL\" new-line , switch to the next row.", "text": "-\"NL\" new-line , switch to the next row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/156", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76504516601562, "t": 147.8971405029297, "r": 480.5928039550781, "b": 127.14515686035156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 99]}], "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML.", "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}, {"self_ref": "#/texts/157", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/158", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/159", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 134.76499938964844, "t": 666.2008056640625, "r": 480.58740234375, "b": 636.1503295898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 207]}], "orig": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding"}, {"self_ref": "#/texts/160", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 229.81627, "t": 625.48505, "r": 233.49992000000003, "b": 619.27057, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/161", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 435.16009999999994, "t": 624.30988, "r": 447.86273, "b": 614.9881, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "NL", "text": "NL"}, {"self_ref": "#/texts/162", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 169.74728, "t": 624.11774, "r": 175.72659, "b": 616.34961, "coord_origin": "BOTTOMLEFT"}, 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Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/231", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 420.9151916503906, "r": 480.59228515625, "b": 400.15325927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 397.002197265625, "r": 226.0736083984375, "b": 388.19525146484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "3. Cross cell rule :", "text": "3. Cross cell rule :", "level": 1}, {"self_ref": "#/texts/233", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 151.70098876953125, "t": 385.0332336425781, "r": 480.5923767089844, "b": 352.3262939453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 167]}], "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/234", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 349.17425537109375, "r": 474.5901794433594, "b": 340.3673095703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 78]}], "orig": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 337.21624755859375, "r": 480.58746337890625, "b": 316.4543151855469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 313.3032531738281, "r": 480.5945739746094, "b": 292.5403137207031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 144]}], "orig": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76498413085938, "t": 279.40728759765625, "r": 480.5958251953125, "b": 151.05833435058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 848]}], "orig": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid.", "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"self_ref": "#/texts/238", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76498413085938, "t": 147.89730834960938, "r": 480.5926513671875, "b": 127.14533233642578, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern", "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}, {"self_ref": "#/texts/239", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "8", "text": "8"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/241", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5888366699219, "b": 652.314208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "reduces significantly the column drift seen in the HTML based models (see Figure 5).", "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"self_ref": "#/texts/242", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 630.4431762695312, "r": 319.3470764160156, "b": 621.63623046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "4.3 Error-detection and -mitigation", "text": "4.3 Error-detection and -mitigation", "level": 1}, {"self_ref": "#/texts/243", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 609.7182006835938, "r": 480.59576416015625, "b": 493.32415771484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 797]}], "orig": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied.", "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"self_ref": "#/texts/244", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 470.83599853515625, "r": 229.03533935546875, "b": 460.2676086425781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "5 Experiments", "text": "5 Experiments", "level": 1}, {"self_ref": "#/texts/245", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 444.7501525878906, "r": 480.59527587890625, "b": 340.3122863769531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 684]}], "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available.", "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 134.7650146484375, "t": 307.35186767578125, "r": 480.5908203125, "b": 288.2603454589844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"self_ref": "#/texts/247", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 332.47852, "t": 283.85562, "r": 348.14014, "b": 279.59244, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "BBoxes", "text": "BBoxes"}, {"self_ref": "#/texts/248", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 273.61066, "t": 282.0947, "r": 284.47275, "b": 277.83151, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "BBox", "text": "BBox"}, {"self_ref": "#/texts/249", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 270.45187, "t": 278.30716000000007, "r": 287.63242, "b": 274.0439799999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Decoder", "text": "Decoder"}, {"self_ref": "#/texts/250", 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{"self_ref": "#/texts/317", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 346.76874, "t": 208.59932000000003, "r": 350.60995, "b": 203.60892, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/318", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 353.26935, "t": 208.59371999999996, "r": 360.0697, "b": 203.60331999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "NL", "text": "NL"}, {"self_ref": "#/texts/319", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 171.80722045898438, "r": 480.59173583984375, "b": 127.1452407836914, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 299]}], "orig": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in", "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}, {"self_ref": "#/texts/320", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/321", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9"}, {"self_ref": "#/texts/322", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5957946777344, "b": 640.3582153320312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 163]}], "orig": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"self_ref": "#/texts/323", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 622.8141479492188, "r": 318.44842529296875, "b": 614.0072021484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "level": 1}, {"self_ref": "#/texts/324", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76498413085938, "t": 606.4141845703125, "r": 480.5927734375, "b": 537.8411254882812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 423]}], "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"self_ref": "#/texts/325", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 134.76498413085938, "t": 516.9276733398438, "r": 480.59539794921875, "b": 464.9591979980469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 398]}], "orig": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"self_ref": "#/texts/326", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 283.84820556640625, "r": 264.4033203125, "b": 275.041259765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "level": 1}, {"self_ref": "#/texts/327", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 267.44921875, "r": 480.59576416015625, "b": 174.9652557373047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 555]}], "orig": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"self_ref": "#/texts/328", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 171.80722045898438, "r": 480.59576416015625, "b": 127.1452407836914, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 289]}], "orig": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}, {"self_ref": "#/texts/329", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 143.97886657714844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/330", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 10, "bbox": {"l": 167.82052612304688, "t": 698.22900390625, "r": 231.72048950195312, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/331", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 676.163818359375, "r": 480.59356689453125, "b": 646.1133422851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 192]}], "orig": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).", "text": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8)."}, {"self_ref": "#/texts/332", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 503.085205078125, "r": 257.0867919921875, "b": 494.27825927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "5.3 Qualitative Results", "text": "5.3 Qualitative Results", "level": 1}, {"self_ref": "#/texts/333", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 482.13922119140625, "r": 480.5898132324219, "b": 425.5223083496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes.", "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes."}, {"self_ref": "#/texts/334", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 394.4098815917969, "r": 480.591064453125, "b": 352.2828369140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 270]}], "orig": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc", "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc"}, {"self_ref": "#/texts/335", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 412.78879, "t": 344.00702, "r": 414.93463, "b": 334.20035000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "\u2265", "text": "\u2265"}, {"self_ref": "#/texts/336", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 261.20892, "t": 343.53876, "r": 263.56973, "b": 340.80273, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "S", "text": "S"}, {"self_ref": "#/texts/337", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 312.33463, "t": 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"label": "page_header", "prov": [{"page_no": 11, "bbox": {"l": 471.3756103515625, "t": 698.22900390625, "r": 480.5894775390625, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/436", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 134.76499938964844, "t": 666.2008056640625, "r": 480.58837890625, "b": 614.2323608398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 390]}], "orig": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"self_ref": "#/texts/437", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 247.83432, "t": 607.24011, "r": 253.61339, "b": 597.18365, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/438", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 181.89114, "t": 503.64037999999994, "r": 239.23492, "b": 497.7052299999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "Repeating pattern of", "text": "Repeating pattern of"}, {"self_ref": "#/texts/439", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 181.89114, "t": 497.10577, "r": 251.52917, "b": 491.17062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "horizontally merged cells", "text": "horizontally merged cells"}, {"self_ref": "#/texts/440", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 171.5049, "t": 479.54968, "r": 177.59613, "b": 471.63614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/441", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 172.27777, "t": 410.63712, "r": 180.18666, "b": 388.59933, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "OTSL", "text": "OTSL"}, {"self_ref": "#/texts/442", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 293.64209, "t": 326.40216, "r": 437.50800000000004, "b": 320.46701, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Repeating pattern is well represented in predictions", "text": "Repeating pattern is well represented in predictions"}, {"self_ref": "#/texts/443", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 171.05823, "t": 299.34726, "r": 177.14946, "b": 291.43372, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/444", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 172.27747, "t": 236.22305000000006, "r": 180.18663, "b": 213.25220000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "HTML", "text": "HTML"}, {"self_ref": "#/texts/445", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 292.18976, "t": 184.19390999999996, "r": 381.54663, "b": 178.25875999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Bounding box drifting at the end", "text": "Bounding box drifting at the end"}, {"self_ref": "#/texts/446", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 283.047, "t": 174.64224000000002, "r": 398.05978, "b": 168.70709, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Horizontally merged cells are not present", "text": "Horizontally merged cells are not present"}, {"self_ref": "#/texts/447", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 283.047, "t": 164.51833999999997, "r": 374.96332, "b": 158.58319000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Incorrect end of HTML sequence", "text": "Incorrect end of HTML sequence"}, {"self_ref": "#/texts/448", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 143.97886657714844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "12", "text": "12"}, {"self_ref": "#/texts/449", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 12, "bbox": {"l": 167.82052612304688, "t": 698.22900390625, "r": 231.72048950195312, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/450", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 674.4510498046875, "r": 219.25479125976562, "b": 663.8826293945312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "6 Conclusion", "text": "6 Conclusion", "level": 1}, {"self_ref": "#/texts/451", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 645.13623046875, "r": 480.595703125, "b": 588.5181884765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 330]}], "orig": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits.", "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"self_ref": "#/texts/452", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 584.5562133789062, "r": 480.59478759765625, "b": 468.1632080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 724]}], "orig": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1).", "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"self_ref": "#/texts/453", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 464.201171875, "r": 480.5948181152344, "b": 323.8973388671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 926]}], "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation.", "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"self_ref": "#/texts/454", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 298.1791687011719, "r": 197.68641662597656, "b": 287.61077880859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "References", "text": "References", "level": 1}, {"self_ref": "#/texts/455", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 139.37100219726562, "t": 269.1201477050781, "r": 480.5920104980469, "b": 228.12855529785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 270]}], "orig": "1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. 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"charspan": [0, 36]}], "orig": "{mly,ahn,nli,cau,taa}@zurich.ibm.com", "text": "{mly,ahn,nli,cau,taa}@zurich.ibm.com"}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 163.11109924316406, "t": 521.6988525390625, "r": 452.248779296875, "b": 327.2655334472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1198]}], "orig": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community.", "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 163.11109924316406, "t": 313.3060607910156, "r": 452.2415771484375, "b": 294.2145080566406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization.", "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 269.88031005859375, "r": 228.933837890625, "b": 259.3119201660156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "1 Introduction", "text": "1 Introduction", "level": 1}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 243.7134552001953, "r": 480.595947265625, "b": 163.18548583984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 500]}], "orig": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods.", "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 159.85244750976562, "r": 480.5958251953125, "b": 127.14546966552734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 235]}], "orig": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of", "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of"}, {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 665.6658325195312, "r": 480.5918884277344, "b": 591.7794189453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 574]}], "orig": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).", "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"self_ref": "#/texts/13", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 154.3298, "t": 578.42542, "r": 159.79837, "b": 571.3208, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/14", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 321.07053, "t": 578.42542, "r": 326.53909, "b": 571.3208, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/15", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 384.11816, "t": 533.45282, "r": 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"label": "text", "prov": [{"page_no": 2, "bbox": {"l": 407.42249, "t": 380.66647, "r": 412.47598, "b": 372.14096, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "L", "text": "L"}, {"self_ref": "#/texts/113", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 418.69287, "t": 380.66647, "r": 425.25516, "b": 372.14096, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "U", "text": "U"}, {"self_ref": "#/texts/114", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 430.5086099999999, "t": 380.66647, "r": 436.5709800000001, "b": 372.14096, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "X", "text": "X"}, {"self_ref": "#/texts/115", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 152.36208, "t": 373.89478, "r": 190.74123, "b": 367.50064, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "rowspan=\"2\"", "text": "rowspan=\"2\""}, {"self_ref": "#/texts/116", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 194.52863, "t": 373.89478, "r": 232.90777999999997, "b": 367.50064, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "rowspan=\"3\"", "text": "rowspan=\"3\""}, {"self_ref": "#/texts/117", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 236.69518999999997, "t": 373.89478, "r": 240.67617999999996, "b": 367.50064, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ">", "text": ">"}, {"self_ref": "#/texts/118", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 244.46358, "t": 373.89478, "r": 269.10144, "b": 367.50064, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "</table>", "text": "</table>"}, {"self_ref": "#/texts/119", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 339.68621826171875, "r": 480.5923156738281, "b": 271.1133117675781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 435]}], "orig": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"self_ref": "#/texts/120", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 134.7650146484375, "t": 267.44927978515625, "r": 480.5948181152344, "b": 127.14530181884766, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 911]}], "orig": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR.", "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}, {"self_ref": "#/texts/121", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/122", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 673.0662231445312, "r": 480.5918273925781, "b": 580.5831298828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 584]}], "orig": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments.", "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"self_ref": "#/texts/124", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 577.1641235351562, "r": 480.5957336425781, "b": 460.7701416015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 721]}], "orig": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML.", "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"self_ref": "#/texts/125", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 457.35211181640625, "r": 480.5956726074219, "b": 352.9132385253906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 626]}], "orig": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps.", "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"self_ref": "#/texts/126", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 329.91204833984375, "r": 236.76913452148438, "b": 319.3436584472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "2 Related Work", "text": "2 Related Work", "level": 1}, {"self_ref": "#/texts/127", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 303.3141784667969, "r": 484.1204833984375, "b": 127.14423370361328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1161]}], "orig": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell.", "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}, {"self_ref": "#/texts/128", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/129", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/130", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.59576416015625, "b": 532.7620849609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 939]}], "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"self_ref": "#/texts/131", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 529.3430786132812, "r": 480.595703125, "b": 305.3533020019531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1404]}], "orig": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content.", "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"self_ref": "#/texts/132", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 301.93426513671875, "r": 480.5937805175781, "b": 209.4513397216797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 572]}], "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task.", "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"self_ref": "#/texts/133", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 186.45016479492188, "r": 269.6244201660156, "b": 175.88177490234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "3 Problem Statement", "text": "3 Problem Statement", "level": 1}, {"self_ref": "#/texts/134", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 159.85231018066406, "r": 480.59368896484375, "b": 127.14434051513672, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 233]}], "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-", "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}, {"self_ref": "#/texts/135", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/136", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/137", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5937805175781, "b": 604.4931640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 422]}], "orig": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary.", "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"self_ref": "#/texts/138", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 145.6070098876953, "t": 570.9207153320312, "r": 469.7522277832031, "b": 562.7882080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"self_ref": "#/texts/139", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.76499938964844, "t": 423.793212890625, "r": 480.5947570800781, "b": 259.57940673828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1021]}], "orig": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure.", "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"self_ref": "#/texts/140", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.7650146484375, "t": 255.95736694335938, "r": 480.5928955078125, "b": 211.29440307617188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 313]}], "orig": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible.", "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"self_ref": "#/texts/141", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.7650146484375, "t": 207.67337036132812, "r": 480.5947265625, "b": 127.14539337158203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 542]}], "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence", "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"self_ref": "#/texts/142", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6"}, {"self_ref": "#/texts/143", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/144", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.59478759765625, "b": 652.314208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 132]}], "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output.", "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"self_ref": "#/texts/145", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 648.5172119140625, "r": 480.595703125, "b": 496.2580871582031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 977]}], "orig": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content.", "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"self_ref": "#/texts/146", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 471.368896484375, "r": 372.50848388671875, "b": 460.8005065917969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "4 Optimised Table Structure Language", "text": "4 Optimised Table Structure Language", "level": 1}, {"self_ref": "#/texts/147", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 442.8830261230469, "r": 480.5947265625, "b": 350.400146484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 563]}], "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture.", "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"self_ref": "#/texts/148", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 326.1280822753906, "r": 261.80108642578125, "b": 317.3211364746094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "4.1 Language Definition", "text": "4.1 Language Definition", "level": 1}, {"self_ref": "#/texts/149", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 303.0021057128906, "r": 480.5887145996094, "b": 270.2941589355469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 165]}], "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid.", "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"self_ref": "#/texts/150", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 149.708984375, "t": 266.4981384277344, "r": 409.3113708496094, "b": 257.701171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "The OTSL vocabulary is comprised of the following tokens:", "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"self_ref": "#/texts/151", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.99298095703125, "t": 244.0301055908203, "r": 460.54443359375, "b": 235.22317504882812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "-\"C\" cell a new table cell that either has or does not have cell content", "text": "-\"C\" cell a new table cell that either has or does not have cell content", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/152", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.99301147460938, "t": 231.43710327148438, "r": 480.59393310546875, "b": 210.6751708984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/153", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 206.8881072998047, "r": 480.58856201171875, "b": 186.1261749267578, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/154", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 182.34010314941406, "r": 454.5549621582031, "b": 173.53317260742188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells", "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/155", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 169.74610900878906, "r": 328.61676025390625, "b": 160.93917846679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "-\"NL\" new-line , switch to the next row.", "text": "-\"NL\" new-line , switch to the next row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/156", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76504516601562, "t": 147.8971405029297, "r": 480.5928039550781, "b": 127.14515686035156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 99]}], "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML.", "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}, {"self_ref": "#/texts/157", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/158", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/159", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 134.76499938964844, "t": 666.2008056640625, "r": 480.58740234375, "b": 636.1503295898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 207]}], "orig": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding"}, {"self_ref": "#/texts/160", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 229.81627, "t": 625.48505, "r": 233.49992000000003, "b": 619.27057, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/161", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 435.16009999999994, "t": 624.30988, "r": 447.86273, "b": 614.9881, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "NL", "text": "NL"}, {"self_ref": "#/texts/162", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 169.74728, "t": 624.11774, "r": 175.72659, "b": 616.34961, "coord_origin": "BOTTOMLEFT"}, 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1]}], "orig": "L", "text": "L"}, {"self_ref": "#/texts/210", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 334.51135, "t": 539.06744, "r": 337.33313, "b": 532.85297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/211", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 340.15491, "t": 539.06744, "r": 421.98624, "b": 532.85297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "- horizontal merges: \"C\", \"L\"", "text": "- horizontal merges: \"C\", \"L\""}, {"self_ref": "#/texts/212", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 250.17235999999997, "t": 535.56049, "r": 257.34753, "b": 526.23871, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "U", "text": "U"}, {"self_ref": "#/texts/213", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 294.9021, "t": 535.29846, "r": 301.03976, "b": 525.97668, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "X", "text": "X"}, {"self_ref": "#/texts/214", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 307.17743, "t": 535.29846, "r": 325.59039, "b": 525.97668, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "X X", "text": "X X"}, {"self_ref": "#/texts/215", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 282.11035, "t": 535.14978, "r": 289.28552, "b": 525.828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "U", "text": "U"}, {"self_ref": "#/texts/216", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 334.51135, "t": 529.12952, "r": 337.29868, "b": 522.91504, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/217", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 340.086, "t": 529.12952, "r": 415.34375, "b": 522.91504, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "- vertical merges: \"C\", \"U\"", "text": "- vertical merges: \"C\", \"U\""}, {"self_ref": "#/texts/218", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 185.96759, "t": 523.65234, "r": 189.65125, "b": 517.43787, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/219", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 232.44858, "t": 523.32043, "r": 237.97405999999998, "b": 513.99866, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "L", "text": "L"}, {"self_ref": "#/texts/220", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 250.47049000000004, "t": 523.27777, "r": 257.64566, "b": 513.95599, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "U", "text": "U"}, {"self_ref": "#/texts/221", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 209.20891, "t": 523.26733, "r": 214.73439, "b": 513.94556, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "L", "text": "L"}, {"self_ref": "#/texts/222", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 195.93939, "t": 523.25201, "r": 203.11456, "b": 513.93024, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/223", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 221.14551, "t": 523.18707, "r": 226.67099, "b": 513.8653, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "L", "text": "L"}, {"self_ref": "#/texts/224", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 282.40848, "t": 522.867, "r": 289.58365, "b": 513.54523, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "U", "text": "U"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 294.78949, "t": 522.74579, "r": 300.92715, "b": 513.42401, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "X", "text": "X"}, {"self_ref": "#/texts/226", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 307.06482, "t": 522.74579, "r": 325.47778, "b": 513.42401, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "X X", "text": "X X"}, {"self_ref": "#/texts/227", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 334.51135, "t": 519.19159, "r": 426.59875, "b": 512.97711, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "4 - 2d merges: \"C\", \"L\", \"U\", \"X\"", "text": "4 - 2d merges: \"C\", \"L\", \"U\", \"X\"", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/228", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 134.76499938964844, "t": 486.7041931152344, "r": 246.6519775390625, "b": 477.8972473144531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "4.2 Language Syntax", "text": "4.2 Language Syntax", "level": 1}, {"self_ref": "#/texts/229", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76499938964844, "t": 466.7522277832031, "r": 363.7961730957031, "b": 457.95526123046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "The OTSL representation follows these syntax rules:", "text": "The OTSL representation follows these syntax rules:"}, {"self_ref": "#/texts/230", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 444.8291931152344, "r": 480.5890197753906, "b": 424.0662536621094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 108]}], "orig": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/231", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 420.9151916503906, "r": 480.59228515625, "b": 400.15325927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 397.002197265625, "r": 226.0736083984375, "b": 388.19525146484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "3. Cross cell rule :", "text": "3. Cross cell rule :", "level": 1}, {"self_ref": "#/texts/233", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 151.70098876953125, "t": 385.0332336425781, "r": 480.5923767089844, "b": 352.3262939453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 167]}], "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/234", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 349.17425537109375, "r": 474.5901794433594, "b": 340.3673095703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 78]}], "orig": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 337.21624755859375, "r": 480.58746337890625, "b": 316.4543151855469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 313.3032531738281, "r": 480.5945739746094, "b": 292.5403137207031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 144]}], "orig": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76498413085938, "t": 279.40728759765625, "r": 480.5958251953125, "b": 151.05833435058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 848]}], "orig": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid.", "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"self_ref": "#/texts/238", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76498413085938, "t": 147.89730834960938, "r": 480.5926513671875, "b": 127.14533233642578, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern", "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}, {"self_ref": "#/texts/239", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "8", "text": "8"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/241", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5888366699219, "b": 652.314208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "reduces significantly the column drift seen in the HTML based models (see Figure 5).", "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"self_ref": "#/texts/242", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 630.4431762695312, "r": 319.3470764160156, "b": 621.63623046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "4.3 Error-detection and -mitigation", "text": "4.3 Error-detection and -mitigation", "level": 1}, {"self_ref": "#/texts/243", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 609.7182006835938, "r": 480.59576416015625, "b": 493.32415771484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 797]}], "orig": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied.", "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"self_ref": "#/texts/244", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 470.83599853515625, "r": 229.03533935546875, "b": 460.2676086425781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "5 Experiments", "text": "5 Experiments", "level": 1}, {"self_ref": "#/texts/245", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 444.7501525878906, "r": 480.59527587890625, "b": 340.3122863769531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 684]}], "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available.", "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 134.7650146484375, "t": 307.35186767578125, "r": 480.5908203125, "b": 288.2603454589844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"self_ref": "#/texts/247", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 332.47852, "t": 283.85562, "r": 348.14014, "b": 279.59244, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "BBoxes", "text": "BBoxes"}, {"self_ref": "#/texts/248", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 273.61066, "t": 282.0947, "r": 284.47275, "b": 277.83151, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "BBox", "text": "BBox"}, {"self_ref": "#/texts/249", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 270.45187, "t": 278.30716000000007, "r": 287.63242, "b": 274.0439799999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Decoder", "text": "Decoder"}, {"self_ref": "#/texts/250", 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"coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Structure Tags", "text": "Structure Tags"}, {"self_ref": "#/texts/314", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 293.94702, "t": 209.56352000000004, "r": 324.59396, "b": 205.30030999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "in OTSL format", "text": "in OTSL format"}, {"self_ref": "#/texts/315", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 333.56549, "t": 208.64526, "r": 337.40671, "b": 203.65485999999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/316", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 340.27948, "t": 208.60262999999998, "r": 344.1207, "b": 203.61222999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/317", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 346.76874, "t": 208.59932000000003, "r": 350.60995, "b": 203.60892, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/318", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 353.26935, "t": 208.59371999999996, "r": 360.0697, "b": 203.60331999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "NL", "text": "NL"}, {"self_ref": "#/texts/319", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 171.80722045898438, "r": 480.59173583984375, "b": 127.1452407836914, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 299]}], "orig": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in", "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}, {"self_ref": "#/texts/320", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/321", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9"}, {"self_ref": "#/texts/322", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5957946777344, "b": 640.3582153320312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 163]}], "orig": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"self_ref": "#/texts/323", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 622.8141479492188, "r": 318.44842529296875, "b": 614.0072021484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "level": 1}, {"self_ref": "#/texts/324", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76498413085938, "t": 606.4141845703125, "r": 480.5927734375, "b": 537.8411254882812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 423]}], "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"self_ref": "#/texts/325", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 134.76498413085938, "t": 516.9276733398438, "r": 480.59539794921875, "b": 464.9591979980469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 398]}], "orig": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"self_ref": "#/texts/326", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 283.84820556640625, "r": 264.4033203125, "b": 275.041259765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "level": 1}, {"self_ref": "#/texts/327", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 267.44921875, "r": 480.59576416015625, "b": 174.9652557373047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 555]}], "orig": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"self_ref": "#/texts/328", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 171.80722045898438, "r": 480.59576416015625, "b": 127.1452407836914, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 289]}], "orig": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}, {"self_ref": "#/texts/329", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 143.97886657714844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/330", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 10, "bbox": {"l": 167.82052612304688, "t": 698.22900390625, "r": 231.72048950195312, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/331", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 676.163818359375, "r": 480.59356689453125, "b": 646.1133422851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 192]}], "orig": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).", "text": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8)."}, {"self_ref": "#/texts/332", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 503.085205078125, "r": 257.0867919921875, "b": 494.27825927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "5.3 Qualitative Results", "text": "5.3 Qualitative Results", "level": 1}, {"self_ref": "#/texts/333", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 482.13922119140625, "r": 480.5898132324219, "b": 425.5223083496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes.", "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes."}, {"self_ref": "#/texts/334", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 394.4098815917969, "r": 480.591064453125, "b": 352.2828369140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 270]}], "orig": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc", "text": "Fig. 5. 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"label": "page_header", "prov": [{"page_no": 11, "bbox": {"l": 471.3756103515625, "t": 698.22900390625, "r": 480.5894775390625, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/436", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 134.76499938964844, "t": 666.2008056640625, "r": 480.58837890625, "b": 614.2323608398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 390]}], "orig": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"self_ref": "#/texts/437", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 247.83432, "t": 607.24011, "r": 253.61339, "b": 597.18365, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/438", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 181.89114, "t": 503.64037999999994, "r": 239.23492, "b": 497.7052299999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "Repeating pattern of", "text": "Repeating pattern of"}, {"self_ref": "#/texts/439", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 181.89114, "t": 497.10577, "r": 251.52917, "b": 491.17062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "horizontally merged cells", "text": "horizontally merged cells"}, {"self_ref": "#/texts/440", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 171.5049, "t": 479.54968, "r": 177.59613, "b": 471.63614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/441", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 172.27777, "t": 410.63712, "r": 180.18666, "b": 388.59933, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "OTSL", "text": "OTSL"}, {"self_ref": "#/texts/442", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 293.64209, "t": 326.40216, "r": 437.50800000000004, "b": 320.46701, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Repeating pattern is well represented in predictions", "text": "Repeating pattern is well represented in predictions"}, {"self_ref": "#/texts/443", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 171.05823, "t": 299.34726, "r": 177.14946, "b": 291.43372, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/444", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 172.27747, "t": 236.22305000000006, "r": 180.18663, "b": 213.25220000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "HTML", "text": "HTML"}, {"self_ref": "#/texts/445", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 292.18976, "t": 184.19390999999996, "r": 381.54663, "b": 178.25875999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Bounding box drifting at the end", "text": "Bounding box drifting at the end"}, {"self_ref": "#/texts/446", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 283.047, "t": 174.64224000000002, "r": 398.05978, "b": 168.70709, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Horizontally merged cells are not present", "text": "Horizontally merged cells are not present"}, {"self_ref": "#/texts/447", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 283.047, "t": 164.51833999999997, "r": 374.96332, "b": 158.58319000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Incorrect end of HTML sequence", "text": "Incorrect end of HTML sequence"}, {"self_ref": "#/texts/448", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 143.97886657714844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "12", "text": "12"}, {"self_ref": "#/texts/449", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 12, "bbox": {"l": 167.82052612304688, "t": 698.22900390625, "r": 231.72048950195312, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/450", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 674.4510498046875, "r": 219.25479125976562, "b": 663.8826293945312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "6 Conclusion", "text": "6 Conclusion", "level": 1}, {"self_ref": "#/texts/451", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 645.13623046875, "r": 480.595703125, "b": 588.5181884765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 330]}], "orig": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits.", "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"self_ref": "#/texts/452", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 584.5562133789062, "r": 480.59478759765625, "b": 468.1632080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 724]}], "orig": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1).", "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"self_ref": "#/texts/453", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 464.201171875, "r": 480.5948181152344, "b": 323.8973388671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 926]}], "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation.", "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"self_ref": "#/texts/454", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 298.1791687011719, "r": 197.68641662597656, "b": 287.61077880859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "References", "text": "References", "level": 1}, {"self_ref": "#/texts/455", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 139.37100219726562, "t": 269.1201477050781, "r": 480.5920104980469, "b": 228.12855529785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 270]}], "orig": "1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. 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"b": 557.2963256835938, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "3.26", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 148.62600708007812, "t": 546.5370483398438, "r": 207.15240478515625, "b": 538.4673461914062, "coord_origin": "BOTTOMLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "PubTables-1M", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 222.43699645996094, "t": 552.0170288085938, "r": 247.13226318359375, "b": 543.9473266601562, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "OTSL", "column_header": false, "row_header": true, "row_section": false}, 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\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1.md b/tests/data/groundtruth/docling_v2/2305.03393v1.md
index bd829209..d716aa27 100644
--- a/tests/data/groundtruth/docling_v2/2305.03393v1.md
+++ b/tests/data/groundtruth/docling_v2/2305.03393v1.md
@@ -126,12 +126,12 @@ Table 1. HPO performed in OTSL and HTML representation on the same transformer-b
 
 | #          | #          | Language   | TEDs        | TEDs        | TEDs        | mAP         | Inference   |
 |------------|------------|------------|-------------|-------------|-------------|-------------|-------------|
-| enc-layers | dec-layers |            | simple      | complex     | all         | (0.75)      | time (secs) |
+| enc-layers | dec-layers | Language   | simple      | complex     | all         | (0.75)      | time (secs) |
 | 6          | 6          | OTSL HTML  | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857  | 2.73 5.39   |
-| 4          | 4          | OTSL       | 0.938       | 0.904       | 0.927       | 0.853       | 1.97        |
-|            |            | HTML       | 0.952       | 0.909       | 0.938       | 0.843       | 3.77        |
-| 2          | 4          | OTSL HTML  | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91        |
-| 4          | 2          | OTSL HTML  | 0.952 0.944 | 0.92 0.903  | 0.942 0.931 | 0.857 0.824 | 3.81 1.22 2 |
+| 4          | 4          | OTSL HTML  | 0.938 0.952 | 0.904       | 0.927       | 0.853       | 1.97        |
+| 2          | 4          | OTSL       | 0.923 0.945 | 0.909 0.897 | 0.938       | 0.843       | 3.77        |
+|            |            | HTML       |             | 0.901       | 0.915 0.931 | 0.859 0.834 | 1.91 3.81   |
+| 4          | 2          | OTSL HTML  | 0.952 0.944 | 0.92 0.903  | 0.942 0.931 | 0.857 0.824 | 1.22 2      |
 
 ## 5.2 Quantitative Results
 
@@ -141,15 +141,15 @@ Additionally, the results show that OTSL has an advantage over HTML when applied
 
 Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).
 
-|              | Language   | TEDs   | TEDs    | TEDs   | mAP(0.75)   | Inference   |
-|--------------|------------|--------|---------|--------|-------------|-------------|
-| Data set     |            | simple | complex | all    |             | time (secs) |
-| PubTabNet    | OTSL       | 0.965  | 0.934   | 0.955  | 0.88        | 2.73        |
-| PubTabNet    | HTML       | 0.969  | 0.927   | 0.955  | 0.857       | 5.39        |
-| FinTabNet    | OTSL       | 0.955  | 0.961   | 0.959  | 0.862       | 1.85        |
-| FinTabNet    | HTML       | 0.917  | 0.922   | 0.92   | 0.722       | 3.26        |
-| PubTables-1M | OTSL       | 0.987  | 0.964   | 0.977  | 0.896       | 1.79        |
-| PubTables-1M | HTML       | 0.983  | 0.944   | 0.966  | 0.889       | 3.26        |
+|              | Language   | TEDs   | TEDs    | TEDs   | mAP(0.75)   | Inference time (secs)   |
+|--------------|------------|--------|---------|--------|-------------|-------------------------|
+|              | Language   | simple | complex | all    | mAP(0.75)   | Inference time (secs)   |
+| PubTabNet    | OTSL       | 0.965  | 0.934   | 0.955  | 0.88        | 2.73                    |
+| PubTabNet    | HTML       | 0.969  | 0.927   | 0.955  | 0.857       | 5.39                    |
+| FinTabNet    | OTSL       | 0.955  | 0.961   | 0.959  | 0.862       | 1.85                    |
+| FinTabNet    | HTML       | 0.917  | 0.922   | 0.92   | 0.722       | 3.26                    |
+| PubTables-1M | OTSL       | 0.987  | 0.964   | 0.977  | 0.896       | 1.79                    |
+| PubTables-1M | HTML       | 0.983  | 0.944   | 0.966  | 0.889       | 3.26                    |
 
 ## 5.3 Qualitative Results
 
diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1.pages.json b/tests/data/groundtruth/docling_v2/2305.03393v1.pages.json
index 48a6ba8f..88df3ac7 100644
--- a/tests/data/groundtruth/docling_v2/2305.03393v1.pages.json
+++ b/tests/data/groundtruth/docling_v2/2305.03393v1.pages.json
@@ -1 +1 @@
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"TOPLEFT"}}, {"id": 6, "text": "\u2212", "bbox": {"l": 257.298, "t": 169.69159000000002, "r": 263.5249, "b": 176.27484000000004, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "3723", "bbox": {"l": 263.52499, "t": 170.08209, "r": 279.41132, "b": 176.27484000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "\u2212", "bbox": {"l": 279.41, "t": 169.69159000000002, "r": 285.6369, "b": 176.27484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "$^{6960]}$, Ahmed Nassar[0000", "bbox": {"l": 285.63602, "t": 171.67371000000003, "r": 392.27664, "b": 180.47069999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "\u2212", "bbox": {"l": 392.27502, "t": 169.69159000000002, "r": 398.50192, "b": 176.27484000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "0002", "bbox": {"l": 398.50201, "t": 170.08209, "r": 414.38834, "b": 176.27484000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "\u2212", "bbox": {"l": 414.38702, "t": 169.69159000000002, "r": 420.61392, "b": 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Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 115.89910999999995, "r": 480.59735, "b": 146.51409999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.89183509349823, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure", "bbox": {"l": 134.765, "t": 115.89910999999995, "r": 480.59735, "b": 128.58112000000006, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Recognition", "bbox": {"l": 266.67499, "t": 133.83209, "r": 348.68506, "b": 146.51409999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "key_value_region", "bbox": {"l": 139.34305, "t": 169.69159000000002, "r": 476.01270000000005, "b": 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Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.93040531873703, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. 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Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.93040531873703, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. 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Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.93040531873703, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of"}], "headers": [{"label": "page_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8899644017219543, "cells": [{"id": 74, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "C", "bbox": {"l": 396.41107, "t": 280.98352, "r": 402.97336, "b": 289.50903, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", 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154.50595, "t": 258.60095, "r": 159.62473, "b": 265.70556999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "HTML", "bbox": {"l": 164.74348, "t": 258.60095, "r": 185.21857, "b": 265.70556999999997, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "sequence length:", "bbox": {"l": 164.3548, "t": 266.49707, "r": 222.05352999999997, "b": 273.60168, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "55", "bbox": {"l": 224.15326, "t": 266.49707, "r": 232.57729, "b": 273.60168, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "today,", "bbox": {"l": 134.765, "t": 452.31378, "r": 161.32928, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "table detection", "bbox": {"l": 164.269, "t": 452.31378, "r": 226.28617999999997, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "in documents is a well understood problem, and the latest", "bbox": {"l": 229.992, "t": 452.31378, "r": 480.59232000000003, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "state-of-the-art (SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580380082130432, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.907667338848114, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938881158828735, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "picture", "bbox": {"l": 148.45362854003906, "t": 208.37408447265625, "r": 464.3610534667969, "b": 425.8468322753906, 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(SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580380082130432, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.907667338848114, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938881158828735, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45362854003906, "t": 208.37408447265625, "r": 464.3610534667969, "b": 425.8468322753906, "coord_origin": "TOPLEFT"}, "confidence": 0.9688884615898132, "cells": [], "children": [{"id": 46, "label": "text", "bbox": {"l": 154.3298, "t": 213.57457999999997, "r": 159.79837, "b": 220.67920000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 52, "text": "A", "bbox": {"l": 154.3298, "t": 213.57457999999997, "r": 159.79837, "b": 220.67920000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 47, "label": "text", "bbox": {"l": 321.07053, "t": 213.57457999999997, "r": 326.53909, 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On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "body": [{"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938881158828735, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45362854003906, "t": 208.37408447265625, "r": 464.3610534667969, "b": 425.8468322753906, "coord_origin": "TOPLEFT"}, "confidence": 0.9688884615898132, "cells": [], "children": [{"id": 46, "label": "text", "bbox": {"l": 154.3298, "t": 213.57457999999997, "r": 159.79837, "b": 220.67920000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 52, "text": "A", "bbox": {"l": 154.3298, "t": 213.57457999999997, "r": 159.79837, "b": 220.67920000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 47, "label": "text", "bbox": {"l": 321.07053, "t": 213.57457999999997, "r": 326.53909, 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"coord_origin": "TOPLEFT"}, "confidence": 0.9505027532577515, "cells": [{"id": 118, "text": "today,", "bbox": {"l": 134.765, "t": 452.31378, "r": 161.32928, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "table detection", "bbox": {"l": 164.269, "t": 452.31378, "r": 226.28617999999997, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "in documents is a well understood problem, and the latest", "bbox": {"l": 229.992, "t": 452.31378, "r": 480.59232000000003, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "state-of-the-art (SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "headers": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580380082130432, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.907667338848114, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325800538063049, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439422488212585, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325800538063049, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439422488212585, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "body": [{"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439422488212585, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "headers": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325800538063049, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.78900545835495, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443227767944, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290871620178223, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393904209136963, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.78900545835495, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443227767944, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290871620178223, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393904209136963, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "body": [{"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443227767944, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290871620178223, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393904209136963, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "headers": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.78900545835495, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.926919162273407, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696111440658569, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "picture", "bbox": {"l": 137.41452026367188, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387974739074707, "cells": [], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844798445701599, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.926919162273407, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696111440658569, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41452026367188, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387974739074707, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844798445701599, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}], "body": [{"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41452026367188, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387974739074707, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844798445701599, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}], "headers": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.926919162273407, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696111440658569, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919059038162231, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633575081825256, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860696792603, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671324491500854, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556634783744812, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184003472328186, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438936114311218, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636077642440796, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443691968917847, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919059038162231, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633575081825256, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860696792603, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671324491500854, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556634783744812, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184003472328186, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438936114311218, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636077642440796, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443691968917847, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "body": [{"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633575081825256, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860696792603, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671324491500854, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556634783744812, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184003472328186, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438936114311218, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636077642440796, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443691968917847, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "headers": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919059038162231, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "C", "bbox": {"l": 374.49326, "t": 168.59362999999996, "r": 381.66843, "b": 177.91540999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "C", "bbox": {"l": 398.74011, "t": 168.50005999999996, "r": 405.91528, "b": 177.82183999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "C", "bbox": {"l": 373.76862, "t": 192.92553999999996, "r": 380.94379, "b": 202.24730999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 386.66388, "t": 193.07061999999996, "r": 393.83905, "b": 202.39239999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "C", "bbox": {"l": 386.68707, "t": 205.13756999999998, "r": 393.86224, "b": 214.45934999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "C", "bbox": {"l": 398.65729, "t": 180.73279000000002, "r": 405.83246, "b": 190.05457, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "C", "bbox": {"l": 410.77908, "t": 180.73859000000004, "r": 417.95425, "b": 190.06035999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", "bbox": {"l": 422.90503, "t": 180.65247, "r": 430.08020000000005, "b": 189.97424, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "C", "bbox": {"l": 398.7807, "t": 192.98865, "r": 405.95587, "b": 202.31042000000002, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "C", "bbox": {"l": 410.90164, "t": 192.99487, "r": 418.07681, "b": 202.31664999999998, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "C", "bbox": {"l": 423.02753, "t": 192.909, "r": 430.2027, "b": 202.23077, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "C", "bbox": {"l": 398.78235, "t": 205.31573000000003, "r": 405.95752, "b": 214.63751000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "C", "bbox": {"l": 410.90414, "t": 205.32196, "r": 418.07932, "b": 214.64373999999998, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "C", "bbox": {"l": 423.03003, "t": 205.23614999999995, "r": 430.20520000000005, "b": 214.55791999999997, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "C", "bbox": {"l": 386.50574, "t": 217.03882, "r": 393.68091, "b": 226.36059999999998, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "C", "bbox": {"l": 398.60181, "t": 217.21704, "r": 405.77698, "b": 226.53882, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "C", "bbox": {"l": 410.72275, "t": 217.22321, "r": 417.89792, "b": 226.54498, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "C", "bbox": {"l": 422.84869, "t": 217.13738999999998, "r": 430.02386, "b": 226.45916999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "NL", "bbox": {"l": 435.16009999999994, "t": 167.69011999999998, "r": 447.86273, "b": 177.01189999999997, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "NL", "bbox": {"l": 435.44415, "t": 180.20025999999996, "r": 448.14679, "b": 189.52202999999997, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "NL", "bbox": {"l": 435.46735, "t": 192.49474999999995, "r": 448.16998000000007, "b": 201.81652999999994, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "NL", "bbox": {"l": 435.38202, "t": 204.83025999999995, "r": 448.08466, "b": 214.15204000000006, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "NL", "bbox": {"l": 435.59906, "t": 217.2337, "r": 448.3017, "b": 226.55548, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "U", "bbox": {"l": 374.14957, "t": 205.23492, "r": 381.32474, "b": 214.55669999999998, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "U", "bbox": {"l": 374.0419, "t": 217.14648, "r": 381.21707, "b": 226.46826, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "U", "bbox": {"l": 374.34418, "t": 180.93488000000002, "r": 381.51935, "b": 190.25665000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "L", "bbox": {"l": 387.76285, "t": 168.57788000000005, "r": 393.28833, "b": 177.89966000000004, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "L", "bbox": {"l": 411.86395, "t": 168.06195000000002, "r": 417.38943, "b": 177.38373, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "L", "bbox": {"l": 423.33563, "t": 167.93439, "r": 428.86111, "b": 177.25616000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "X", "bbox": {"l": 387.13593, "t": 180.78576999999996, "r": 393.76453, "b": 190.10753999999997, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "C", "bbox": {"l": 282.2594, "t": 244.50878999999998, "r": 289.43457, "b": 253.83056999999997, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "U", "bbox": {"l": 282.11035, "t": 256.85022000000004, "r": 289.28552, "b": 266.172, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "U", "bbox": {"l": 282.40848, "t": 269.13300000000004, "r": 289.58365, "b": 278.45477000000005, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "L", "bbox": {"l": 295.52902, "t": 244.49347, "r": 301.0545, "b": 253.81525, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "L", "bbox": {"l": 307.46613, "t": 244.57372999999995, "r": 312.99161, "b": 253.89550999999994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "L", "bbox": {"l": 318.76886, "t": 244.44037000000003, "r": 324.29434, "b": 253.76215000000002, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "X", "bbox": {"l": 294.9021, "t": 256.70154, "r": 301.03976, "b": 266.02332, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "X X", "bbox": {"l": 307.17743, "t": 256.70154, "r": 325.59039, "b": 266.02332, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "X", "bbox": {"l": 294.78949, "t": 269.25420999999994, "r": 300.92715, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "X X", "bbox": {"l": 307.06482, "t": 269.25420999999994, "r": 325.47778, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "C", "bbox": {"l": 195.93939, "t": 268.74798999999996, "r": 203.11456, "b": 278.06976, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "L", "bbox": {"l": 209.20891, "t": 268.73267, "r": 214.73439, "b": 278.05444, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "L", "bbox": {"l": 221.14551, "t": 268.81293000000005, "r": 226.67099, "b": 278.13469999999995, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "L", "bbox": {"l": 232.44858, "t": 268.67957, "r": 237.97405999999998, "b": 278.00134, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "C", "bbox": {"l": 196.21715, "t": 244.53961000000004, "r": 203.39232, "b": 253.86139000000003, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "C", "bbox": {"l": 250.32143, "t": 244.09813999999994, "r": 257.49661, "b": 253.41992000000005, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "U", "bbox": {"l": 250.17235999999997, "t": 256.43951000000004, "r": 257.34753, "b": 265.76129000000003, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "U", "bbox": {"l": 250.47049000000004, "t": 268.72222999999997, "r": 257.64566, "b": 278.04400999999996, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "1", "bbox": {"l": 334.51135, "t": 242.99463000000003, "r": 337.22485, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "- simple cells: \"C\"", "bbox": {"l": 339.93835, "t": 242.99463000000003, "r": 391.49472, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 334.51135, "t": 252.93255999999997, "r": 337.33313, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "- horizontal merges: \"C\", \"L\"", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "3", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "- vertical merges: \"C\", \"U\"", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "4", "bbox": {"l": 334.51135, "t": 272.80841, "r": 337.30188, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "- 2d merges: \"C\", \"L\", \"U\", \"X\"", "bbox": {"l": 340.09241, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "1", "bbox": {"l": 185.67178, "t": 244.04224, "r": 189.35544, "b": 250.25671, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "2", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "3", "bbox": {"l": 239.34152, "t": 243.62523999999996, "r": 243.02518, "b": 249.83972000000006, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "4", "bbox": {"l": 271.32852, "t": 243.49390000000005, "r": 275.01218, "b": 249.70836999999995, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "2", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "1", "bbox": {"l": 257.24402, "t": 189.961, "r": 260.92767, "b": 196.17548, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 186.87526, "t": 177.97668, "r": 190.55891, "b": 184.19115999999997, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "4", "bbox": {"l": 196.48746, "t": 169.01520000000005, "r": 200.17111, "b": 175.22968000000003, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "A", "bbox": {"l": 169.74728, "t": 167.88225999999997, "r": 175.72659, "b": 175.65039000000002, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "B", "bbox": {"l": 169.74728, "t": 206.83867999999995, "r": 175.72659, "b": 214.60681, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "C", "bbox": {"l": 274.29419, "t": 168.27972, "r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "D", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "E", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326505661010742, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749722838401794, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": 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rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506187319755554, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247231602668762, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259926080703735, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420595765113831, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785566329956055, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590607285499573, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326505661010742, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749722838401794, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}, {"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 3. 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253.83056999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 36, "text": "C", "bbox": {"l": 282.2594, "t": 244.50878999999998, "r": 289.43457, "b": 253.83056999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 63, "label": "text", "bbox": {"l": 196.21715, "t": 244.53961000000004, "r": 203.39232, "b": 253.86139000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 50, "text": "C", "bbox": {"l": 196.21715, "t": 244.53961000000004, "r": 203.39232, "b": 253.86139000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 53, "label": "text", "bbox": {"l": 307.46613, "t": 244.57372999999995, "r": 312.99161, "b": 253.89550999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 40, "text": "L", "bbox": {"l": 307.46613, "t": 244.57372999999995, "r": 312.99161, "b": 253.89550999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 69, "label": "text", "bbox": {"l": 334.51135, "t": 252.93255999999997, "r": 337.33313, "b": 259.14703, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 56, "text": "2", "bbox": {"l": 334.51135, "t": 252.93255999999997, "r": 337.33313, "b": 259.14703, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 70, "label": "text", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 57, "text": "- horizontal merges: \"C\", \"L\"", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 65, "label": "text", "bbox": {"l": 250.17235999999997, "t": 256.43951000000004, "r": 257.34753, "b": 265.76129000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 52, "text": "U", "bbox": {"l": 250.17235999999997, "t": 256.43951000000004, "r": 257.34753, "b": 265.76129000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 55, "label": "text", "bbox": {"l": 294.9021, "t": 256.70154, "r": 301.03976, "b": 266.02332, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 42, "text": "X", "bbox": {"l": 294.9021, "t": 256.70154, "r": 301.03976, "b": 266.02332, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 56, "label": "text", "bbox": {"l": 307.17743, "t": 256.70154, "r": 325.59039, "b": 266.02332, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 43, "text": "X X", "bbox": {"l": 307.17743, "t": 256.70154, "r": 325.59039, "b": 266.02332, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 50, "label": "text", "bbox": {"l": 282.11035, "t": 256.85022000000004, "r": 289.28552, "b": 266.172, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 37, "text": "U", "bbox": {"l": 282.11035, "t": 256.85022000000004, "r": 289.28552, "b": 266.172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 71, "label": "text", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 58, "text": "3", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 72, "label": "text", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 59, "text": "- vertical merges: \"C\", \"U\"", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 74, "label": "text", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 63, "text": "2", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 62, "label": "text", "bbox": {"l": 232.44858, "t": 268.67957, "r": 237.97405999999998, "b": 278.00134, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 49, "text": "L", "bbox": {"l": 232.44858, "t": 268.67957, "r": 237.97405999999998, "b": 278.00134, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 66, "label": "text", "bbox": {"l": 250.47049000000004, "t": 268.72222999999997, "r": 257.64566, "b": 278.04400999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 53, "text": "U", "bbox": {"l": 250.47049000000004, "t": 268.72222999999997, "r": 257.64566, "b": 278.04400999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 60, "label": "text", "bbox": {"l": 209.20891, "t": 268.73267, "r": 214.73439, "b": 278.05444, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 47, "text": "L", "bbox": {"l": 209.20891, "t": 268.73267, "r": 214.73439, "b": 278.05444, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 59, "label": "text", "bbox": {"l": 195.93939, "t": 268.74798999999996, "r": 203.11456, "b": 278.06976, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 46, "text": "C", "bbox": {"l": 195.93939, "t": 268.74798999999996, "r": 203.11456, "b": 278.06976, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 61, "label": "text", "bbox": {"l": 221.14551, "t": 268.81293000000005, "r": 226.67099, "b": 278.13469999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 48, "text": "L", "bbox": {"l": 221.14551, "t": 268.81293000000005, "r": 226.67099, "b": 278.13469999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 51, "label": "text", "bbox": {"l": 282.40848, "t": 269.13300000000004, "r": 289.58365, "b": 278.45477000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 38, "text": "U", "bbox": {"l": 282.40848, "t": 269.13300000000004, "r": 289.58365, "b": 278.45477000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 57, "label": "text", "bbox": {"l": 294.78949, "t": 269.25420999999994, "r": 300.92715, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 44, "text": "X", "bbox": {"l": 294.78949, "t": 269.25420999999994, "r": 300.92715, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 58, "label": "text", "bbox": {"l": 307.06482, "t": 269.25420999999994, "r": 325.47778, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 45, "text": "X X", "bbox": {"l": 307.06482, "t": 269.25420999999994, "r": 325.47778, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 334.51135, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.5250746011734009, "cells": [{"id": 60, "text": "4", "bbox": {"l": 334.51135, "t": 272.80841, "r": 337.30188, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "- 2d merges: \"C\", \"L\", \"U\", \"X\"", "bbox": {"l": 340.09241, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 134.765, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}, "confidence": 0.9233025312423706, "cells": [{"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.2 Language Syntax"}, {"label": "text", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "text", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}, "confidence": 0.9169168472290039, "cells": [{"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL representation follows these syntax rules:"}, {"label": "list_item", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 138.97299, "t": 347.17081, "r": 480.58902, "b": 367.93375, "coord_origin": "TOPLEFT"}, "confidence": 0.958811342716217, "cells": [{"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506187319755554, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247231602668762, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259926080703735, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420595765113831, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785566329956055, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590607285499573, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "body": [{"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 3. 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337.33313, "b": 259.14703, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 56, "text": "2", "bbox": {"l": 334.51135, "t": 252.93255999999997, "r": 337.33313, "b": 259.14703, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 70, "label": "text", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 57, "text": "- horizontal merges: \"C\", \"L\"", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 65, "label": "text", "bbox": {"l": 250.17235999999997, "t": 256.43951000000004, "r": 257.34753, "b": 265.76129000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 52, "text": "U", "bbox": {"l": 250.17235999999997, "t": 256.43951000000004, "r": 257.34753, "b": 265.76129000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 55, "label": "text", "bbox": {"l": 294.9021, "t": 256.70154, "r": 301.03976, "b": 266.02332, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 42, "text": "X", "bbox": {"l": 294.9021, "t": 256.70154, "r": 301.03976, "b": 266.02332, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 56, "label": "text", "bbox": {"l": 307.17743, "t": 256.70154, "r": 325.59039, "b": 266.02332, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 43, "text": "X X", "bbox": {"l": 307.17743, "t": 256.70154, "r": 325.59039, "b": 266.02332, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 50, "label": "text", "bbox": {"l": 282.11035, "t": 256.85022000000004, "r": 289.28552, "b": 266.172, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 37, "text": "U", "bbox": {"l": 282.11035, "t": 256.85022000000004, "r": 289.28552, "b": 266.172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 71, "label": "text", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 58, "text": "3", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 72, "label": "text", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 59, "text": "- vertical merges: \"C\", \"U\"", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 74, "label": "text", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 63, "text": "2", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 62, "label": "text", "bbox": {"l": 232.44858, "t": 268.67957, "r": 237.97405999999998, "b": 278.00134, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 49, "text": "L", "bbox": {"l": 232.44858, "t": 268.67957, "r": 237.97405999999998, "b": 278.00134, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 66, "label": "text", "bbox": {"l": 250.47049000000004, "t": 268.72222999999997, "r": 257.64566, "b": 278.04400999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 53, "text": "U", "bbox": {"l": 250.47049000000004, "t": 268.72222999999997, "r": 257.64566, "b": 278.04400999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 60, "label": "text", "bbox": {"l": 209.20891, "t": 268.73267, "r": 214.73439, "b": 278.05444, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 47, "text": "L", "bbox": {"l": 209.20891, "t": 268.73267, "r": 214.73439, "b": 278.05444, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 59, "label": "text", "bbox": {"l": 195.93939, "t": 268.74798999999996, "r": 203.11456, "b": 278.06976, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 46, "text": "C", "bbox": {"l": 195.93939, "t": 268.74798999999996, "r": 203.11456, "b": 278.06976, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 61, "label": "text", "bbox": {"l": 221.14551, "t": 268.81293000000005, "r": 226.67099, "b": 278.13469999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 48, "text": "L", "bbox": {"l": 221.14551, "t": 268.81293000000005, "r": 226.67099, "b": 278.13469999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 51, "label": "text", "bbox": {"l": 282.40848, "t": 269.13300000000004, "r": 289.58365, "b": 278.45477000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 38, "text": "U", "bbox": {"l": 282.40848, "t": 269.13300000000004, "r": 289.58365, "b": 278.45477000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 57, "label": "text", "bbox": {"l": 294.78949, "t": 269.25420999999994, "r": 300.92715, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 44, "text": "X", "bbox": {"l": 294.78949, "t": 269.25420999999994, "r": 300.92715, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 58, "label": "text", "bbox": {"l": 307.06482, "t": 269.25420999999994, "r": 325.47778, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 45, "text": "X X", "bbox": {"l": 307.06482, "t": 269.25420999999994, "r": 325.47778, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 334.51135, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.5250746011734009, "cells": [{"id": 60, "text": "4", "bbox": {"l": 334.51135, "t": 272.80841, "r": 337.30188, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "- 2d merges: \"C\", \"L\", \"U\", \"X\"", "bbox": {"l": 340.09241, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 134.765, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}, "confidence": 0.9233025312423706, "cells": [{"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.2 Language Syntax"}, {"label": "text", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "text", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}, "confidence": 0.9169168472290039, "cells": [{"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL representation follows these syntax rules:"}, {"label": "list_item", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 138.97299, "t": 347.17081, "r": 480.58902, "b": 367.93375, "coord_origin": "TOPLEFT"}, "confidence": 0.958811342716217, "cells": [{"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506187319755554, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247231602668762, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259926080703735, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420595765113831, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785566329956055, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590607285499573, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "headers": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326505661010742, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749722838401794, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.", "bbox": {"l": 147.30025, "t": 540.73164, "r": 149.70605, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Item", "bbox": {"l": 150.90895, "t": 540.73164, "r": 155.72055, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Amount", "bbox": {"l": 162.75987, "t": 535.3938, "r": 172.2963, "b": 537.76224, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Names", "bbox": {"l": 147.63603, "t": 535.3661500000001, "r": 155.91753, "b": 537.73459, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1000", "bbox": {"l": 158.48466, "t": 540.73164, "r": 164.10178, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "500", "bbox": {"l": 158.48466, "t": 544.67065, "r": 162.69737, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "3500", "bbox": {"l": 158.48466, "t": 548.91264, "r": 164.10178, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "150", "bbox": {"l": 158.48466, "t": 553.15465, "r": 162.69737, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "unit", "bbox": {"l": 168.81696, "t": 540.73164, "r": 172.88876, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "unit", "bbox": {"l": 168.81696, "t": 544.67065, "r": 172.88876, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "unit", "bbox": {"l": 168.81696, "t": 548.91264, "r": 172.88876, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "unit", "bbox": {"l": 168.81696, "t": 553.15465, "r": 172.88876, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "2.", "bbox": {"l": 147.30025, "t": 544.67065, "r": 149.70605, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Item", "bbox": {"l": 150.90895, "t": 544.67065, "r": 155.72055, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "3.", "bbox": {"l": 147.30025, "t": 548.91264, "r": 149.70605, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Item", "bbox": {"l": 150.90895, "t": 548.91264, "r": 155.72055, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "4.", "bbox": {"l": 147.30025, "t": 553.15465, "r": 149.70605, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Item", "bbox": {"l": 150.90895, "t": 553.15465, "r": 155.72055, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Extracted", "bbox": {"l": 152.05046, "t": 517.0098, "r": 171.24945, "b": 521.27298, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Table Images", "bbox": {"l": 148.13347, "t": 522.3122900000001, "r": 175.16759, "b": 526.57547, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Standardized", "bbox": {"l": 193.53331, "t": 524.51422, "r": 220.31973, "b": 528.7774, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Images", "bbox": {"l": 199.47311, "t": 529.8167100000001, "r": 214.37889, "b": 534.0799, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "BBox", "bbox": {"l": 273.61066, "t": 509.9053, "r": 284.47275, "b": 514.16849, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Decoder", "bbox": {"l": 270.45187, "t": 513.6928399999999, "r": 287.63242, "b": 517.9560200000001, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "BBoxes", "bbox": {"l": 332.47852, "t": 508.14438, "r": 348.14014, "b": 512.40756, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "BBoxes can be", "bbox": {"l": 376.68622, "t": 521.12024, "r": 407.25497, "b": 525.38342, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "traced back to the", "bbox": {"l": 373.90869, "t": 525.66525, "r": 410.03506, "b": 529.92844, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "original image to", "bbox": {"l": 375.29871, "t": 530.21024, "r": 408.64902, "b": 534.47342, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "extract content", "bbox": {"l": 377.06747, "t": 534.75522, "r": 406.88312, "b": 539.01843, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Structure Tags sequence", "bbox": {"l": 383.56683, "t": 563.24176, "r": 433.76544, "b": 567.50497, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "provide full description of", "bbox": {"l": 383.52768, "t": 567.78676, "r": 433.80764999999997, "b": 572.04997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "the table structure", "bbox": {"l": 390.47522, "t": 572.33177, "r": 426.85703, "b": 576.59499, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Structure Tags", "bbox": {"l": 293.94702, "t": 577.89143, "r": 323.1691, "b": 582.15465, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "in OTSL format", "bbox": {"l": 293.94702, "t": 582.43648, "r": 324.59396, "b": 586.69969, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "BBoxes in sync", "bbox": {"l": 333.07819, "t": 541.82269, "r": 364.14691, "b": 546.08591, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "with tag sequence", "bbox": {"l": 333.07819, "t": 545.6102, "r": 369.71542, "b": 549.87341, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Encoder", "bbox": {"l": 232.65881000000002, "t": 515.24139, "r": 249.58894000000004, "b": 519.50458, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "Structure", "bbox": {"l": 269.8219, "t": 545.97102, "r": 288.26279, "b": 550.23424, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Decoder", "bbox": {"l": 270.45187, "t": 549.75851, "r": 287.63242, "b": 554.0217299999999, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "[x1, y2, x2, y2]", "bbox": {"l": 332.17676, "t": 515.91205, "r": 358.11206, "b": 520.17523, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "[x1', y2', x2', y2']", "bbox": {"l": 332.17676, "t": 521.9720500000001, "r": 361.58298, "b": 526.23523, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "[x1'', y2'', x2'', y2'']", "bbox": {"l": 332.17676, "t": 528.03204, "r": 364.76474, "b": 532.29523, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "...", "bbox": {"l": 332.17676, "t": 534.09204, "r": 335.96548, "b": 538.35524, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "1", "bbox": {"l": 326.8894, "t": 516.39508, "r": 329.41641, "b": 520.6582599999999, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "2", "bbox": {"l": 327.04089, "t": 522.4247700000001, "r": 329.5679, "b": 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"text": "C", "bbox": {"l": 340.27948, "t": 583.39737, "r": 344.1207, "b": 588.38777, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "C", "bbox": {"l": 346.76874, "t": 583.40068, "r": 350.60995, "b": 588.39108, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "C", "bbox": {"l": 333.56549, "t": 583.35474, "r": 337.40671, "b": 588.34514, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "NL", "bbox": {"l": 353.03326, "t": 556.8831299999999, "r": 359.83362, "b": 561.87354, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "NL", "bbox": {"l": 353.18604, "t": 563.58044, "r": 359.98639, "b": 568.57085, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "NL", "bbox": {"l": 353.19864, "t": 570.1623500000001, "r": 359.99899, "b": 575.15276, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "NL", "bbox": {"l": 353.1532, "t": 576.76611, "r": 359.95355, "b": 581.75652, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "NL", "bbox": {"l": 353.26935, "t": 583.40628, "r": 360.0697, "b": 588.3966800000001, 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the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301324844360352, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8631827235221863, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464746713638306, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515273571014404, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. 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Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515273571014404, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297955632209778, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.7096710205078, "t": 508.06390380859375, "r": 472.73382568359375, "b": 593.67724609375, "coord_origin": "TOPLEFT"}, "confidence": 0.9303393959999084, "cells": [], "children": [{"id": 34, "label": "text", "bbox": {"l": 332.47852, "t": 508.14438, "r": 348.14014, "b": 512.40756, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 57, "text": "BBoxes", "bbox": {"l": 332.47852, "t": 508.14438, "r": 348.14014, "b": 512.40756, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 273.61066, "t": 509.9053, "r": 284.47275, "b": 514.16849, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 55, "text": "BBox", "bbox": {"l": 273.61066, "t": 509.9053, "r": 284.47275, "b": 514.16849, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, 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null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5917400000001, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9512704014778137, "cells": [{"id": 105, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.58792, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.58871, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}], "body": [{"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464746713638306, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515273571014404, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297955632209778, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 4. 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The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}], "headers": [{"label": "page_header", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301324844360352, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8"}, {"label": "page_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8631827235221863, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. 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508.15179, "r": 149.40205, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378157615662, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8857628107070923, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59579, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579682350158691, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.957078218460083, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 144.592, "t": 341.73495, "r": 470.76056, "b": 467.9617, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 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Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579682350158691, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. 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However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.957078218460083, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 144.592, "t": 341.73495, "r": 470.76056, "b": 467.9617, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 231.43106, "b": 362.75570999999997, 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{"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.5957599999999, "b": 617.03474, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5957599999999, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59579, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579682350158691, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.957078218460083, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 144.592, "t": 341.73495, "r": 470.76056, "b": 467.9617, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 231.43106, "b": 362.75570999999997, 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{"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.5957599999999, "b": 617.03474, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5957599999999, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378157615662, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8857628107070923, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9"}]}}, {"page_no": 9, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "10", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 2.", "bbox": {"l": 134.765, "t": 115.83618000000001, "r": 173.09366, "b": 123.76251000000002, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "TSR and cell detection results compared between OTSL and HTML on", "bbox": {"l": 181.30299, "t": 115.89899000000003, "r": 480.59151999999995, "b": 123.96868999999992, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using Table-", "bbox": {"l": 134.765, "t": 126.85797000000014, "r": 480.59357000000006, "b": 134.92767000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Former [9] (with enc=6, dec=6, heads=8).", "bbox": {"l": 134.765, "t": 137.81696, "r": 305.95691, "b": 145.88666, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Data set", "bbox": {"l": 160.782, "t": 166.55895999999996, "r": 194.99779, "b": 174.62865999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Language", "bbox": {"l": 215.52499000000003, "t": 166.534, "r": 254.04465, "b": 174.6037, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "TEDs", "bbox": {"l": 300.397, "t": 161.07898, "r": 323.99118, "b": 169.14868, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "mAP(0.75)", "bbox": {"l": 370.345, "t": 166.55895999999996, "r": 414.74661, "b": 174.62865999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Inference", "bbox": {"l": 426.737, "t": 161.07898, "r": 463.10830999999996, "b": 169.14868, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "time (secs)", "bbox": {"l": 423.11401, "t": 172.03796, "r": 466.72656, "b": 180.10766999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "simple", "bbox": {"l": 262.41299, "t": 174.03101000000004, "r": 288.0596, "b": 182.10071000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "complex", "bbox": {"l": 296.42899, "t": 174.03101000000004, "r": 329.44687, "b": 182.10071000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "all", "bbox": {"l": 345.03299, "t": 174.03101000000004, "r": 354.75793, "b": 182.10071000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "PubTabNet", "bbox": {"l": 154.53799, "t": 192.85999000000004, "r": 201.24129, "b": 200.92969000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "OTSL", "bbox": {"l": 222.43700000000004, "t": 187.38098000000002, "r": 247.13226000000003, "b": 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In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap 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{"id": 56, "text": "Qualitative Results", "bbox": {"l": 160.85904, "t": 288.91479, "r": 257.08679, "b": 297.72173999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.3 Qualitative Results"}, {"label": "text", "id": 1, "page_no": 9, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 309.86078, "r": 480.58981, "b": 366.47769, "coord_origin": "TOPLEFT"}, "confidence": 0.9834067225456238, "cells": [{"id": 57, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5", "bbox": {"l": 134.765, "t": 309.86078, "r": 480.58777, "b": 318.65775, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "demonstrates less overlap and more accurate bounding boxes with OTSL. In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. 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The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). 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The OTSL model (B) captured repeating pattern of horizontally", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.58823, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 480.5881999999999, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and", "bbox": {"l": 134.765, "t": 158.73895000000005, "r": 480.58838000000003, "b": 166.80864999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 169.69794000000002, "r": 415.84454, "b": 177.76764000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"label": "picture", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 168.39263916015625, "t": 181.96795654296875, "r": 447.35272216796875, "b": 634.003173828125, "coord_origin": "TOPLEFT"}, "confidence": 0.7615750432014465, "cells": [], "children": [{"id": 10, "label": "text", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 15, "text": "A", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 181.89114, "t": 288.35962000000006, "r": 239.23492, "b": 294.2947700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 13, "text": "Repeating pattern of", "bbox": {"l": 181.89114, "t": 288.35962000000006, "r": 239.23492, "b": 294.2947700000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 181.89114, "t": 294.89423, "r": 251.52917, "b": 300.82938, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 14, "text": "horizontally merged cells", "bbox": {"l": 181.89114, "t": 294.89423, "r": 251.52917, "b": 300.82938, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 8, "text": "B", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 172.27777, "t": 381.36288, "r": 180.18666, "b": 403.40067, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 17, "text": "OTSL", "bbox": {"l": 172.27777, "t": 381.36288, "r": 180.18666, "b": 403.40067, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 293.64209, "t": 465.59784, "r": 437.50800000000004, "b": 471.53299, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 12, "text": "Repeating pattern is well represented in predictions", "bbox": {"l": 293.64209, "t": 465.59784, "r": 437.50800000000004, "b": 471.53299, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 9, "text": "C", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 172.27747, "t": 555.7769499999999, "r": 180.18663, "b": 578.7478, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 18, "text": "HTML", "bbox": {"l": 172.27747, "t": 555.7769499999999, "r": 180.18663, "b": 578.7478, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 292.18976, "t": 607.80609, "r": 381.54663, "b": 613.7412400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 16, "text": "Bounding box drifting at the end", "bbox": {"l": 292.18976, "t": 607.80609, "r": 381.54663, "b": 613.7412400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "Horizontally merged cells are not present", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 10, "text": "Incorrect end of HTML sequence", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9301635026931763, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "page_header", "bbox": {"l": 471.37561, "t": 93.77099999999996, "r": 480.5894799999999, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9007187485694885, "cells": [{"id": 1, "text": "11", "bbox": {"l": 471.37561, "t": 93.77099999999996, "r": 480.5894799999999, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. 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IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8610868453979492, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8927640914916992, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156443476676941, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806177020072937, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868756532669067, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403368830680847, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698705077171326, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 139.37097, "t": 567.51884, "r": 480.5920100000001, "b": 608.46561, "coord_origin": "TOPLEFT"}, "confidence": 0.973068118095398, "cells": [{"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 480.58731000000006, "b": 631.18761, "coord_origin": "TOPLEFT"}, "confidence": 0.9617277979850769, "cells": [{"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 139.37097, "t": 634.88084, "r": 480.58826, "b": 664.86761, "coord_origin": "TOPLEFT"}, "confidence": 0.975471019744873, "cells": [{"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8610868453979492, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}, {"label": "page_header", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8927640914916992, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156443476676941, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806177020072937, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868756532669067, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403368830680847, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698705077171326, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785"}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 139.37097, "t": 567.51884, "r": 480.5920100000001, "b": 608.46561, "coord_origin": "TOPLEFT"}, "confidence": 0.973068118095398, "cells": [{"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545561. Springer International Publishing, Cham (2022)"}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 480.58731000000006, "b": 631.18761, "coord_origin": "TOPLEFT"}, "confidence": 0.9617277979850769, "cells": [{"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv preprint arXiv:1908.04729 (2019)"}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 139.37097, "t": 634.88084, "r": 480.58826, "b": 664.86761, "coord_origin": "TOPLEFT"}, "confidence": 0.975471019744873, "cells": [{"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)"}], "body": [{"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156443476676941, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806177020072937, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868756532669067, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403368830680847, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698705077171326, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785"}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 139.37097, "t": 567.51884, "r": 480.5920100000001, "b": 608.46561, "coord_origin": "TOPLEFT"}, "confidence": 0.973068118095398, "cells": [{"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545561. 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Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv preprint arXiv:1908.04729 (2019)"}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 139.37097, "t": 634.88084, "r": 480.58826, "b": 664.86761, "coord_origin": "TOPLEFT"}, "confidence": 0.975471019744873, "cells": [{"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. 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Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 115.89910999999995, "r": 480.59735, "b": 146.51409999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.89183509349823, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure", "bbox": {"l": 134.765, "t": 115.89910999999995, "r": 480.59735, "b": 128.58112000000006, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Recognition", "bbox": {"l": 266.67499, "t": 133.83209, "r": 348.68506, "b": 146.51409999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "key_value_region", "bbox": {"l": 137.36988830566406, "t": 168.1707305908203, "r": 476.8817443847656, 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This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.93040531873703, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. 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"id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 163.1111, "t": 270.30115, "r": 452.24878000000007, "b": 464.73447, "coord_origin": "TOPLEFT"}, "confidence": 0.9807655215263367, "cells": [{"id": 40, "text": "Abstract.", "bbox": {"l": 163.1111, "t": 270.30115, "r": 206.6358, "b": 278.22748, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Extracting tables from documents is a crucial task in any", "bbox": {"l": 211.6171, "t": 270.36395000000005, "r": 452.2447199999999, "b": 278.43364999999994, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "document conversion pipeline. Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.93040531873703, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. 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Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.93040531873703, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of"}], "headers": [{"label": "page_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8899644017219543, "cells": [{"id": 74, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "C", "bbox": {"l": 396.41107, "t": 280.98352, "r": 402.97336, "b": 289.50903, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", 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"vocabulary:", "bbox": {"l": 393.75256, "t": 399.7947700000001, "r": 432.48929, "b": 406.89935, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "5", "bbox": {"l": 434.5896000000001, "t": 399.7947700000001, "r": 438.80083999999994, "b": 406.89935, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "tokens", "bbox": {"l": 440.90573, "t": 399.7947700000001, "r": 463.22235, "b": 406.89935, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "D OTSL", "bbox": {"l": 384.11816, "t": 258.54718, "r": 413.99307, "b": 265.65179, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "sequence length:", "bbox": {"l": 393.75256, "t": 266.67505000000006, "r": 451.45129000000003, "b": 273.77966000000004, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "30", "bbox": {"l": 453.55083999999994, "t": 266.67505000000006, "r": 461.97485, "b": 273.77966000000004, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vocabulary for this table:", "bbox": {"l": 151.79318, "t": 399.76016, "r": 233.89371000000003, "b": 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154.50595, "t": 258.60095, "r": 159.62473, "b": 265.70556999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "HTML", "bbox": {"l": 164.74348, "t": 258.60095, "r": 185.21857, "b": 265.70556999999997, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "sequence length:", "bbox": {"l": 164.3548, "t": 266.49707, "r": 222.05352999999997, "b": 273.60168, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "55", "bbox": {"l": 224.15326, "t": 266.49707, "r": 232.57729, "b": 273.60168, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "today,", "bbox": {"l": 134.765, "t": 452.31378, "r": 161.32928, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "table detection", "bbox": {"l": 164.269, "t": 452.31378, "r": 226.28617999999997, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "in documents is a well understood problem, and the latest", "bbox": {"l": 229.992, "t": 452.31378, "r": 480.59232000000003, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "state-of-the-art (SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580380082130432, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.907667338848114, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938881158828735, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "picture", "bbox": {"l": 148.45362854003906, "t": 208.37408447265625, "r": 464.3610534667969, "b": 425.8468322753906, 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On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580380082130432, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.907667338848114, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938881158828735, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45362854003906, "t": 208.37408447265625, "r": 464.3610534667969, "b": 425.8468322753906, "coord_origin": "TOPLEFT"}, "confidence": 0.9688884615898132, "cells": [], "children": [{"id": 46, "label": "text", "bbox": {"l": 154.3298, "t": 213.57457999999997, "r": 159.79837, "b": 220.67920000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 52, "text": "A", "bbox": {"l": 154.3298, "t": 213.57457999999997, "r": 159.79837, "b": 220.67920000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 47, "label": "text", "bbox": {"l": 321.07053, "t": 213.57457999999997, "r": 326.53909, 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On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "body": [{"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938881158828735, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45362854003906, "t": 208.37408447265625, "r": 464.3610534667969, "b": 425.8468322753906, "coord_origin": "TOPLEFT"}, "confidence": 0.9688884615898132, "cells": [], "children": [{"id": 46, "label": "text", "bbox": {"l": 154.3298, "t": 213.57457999999997, "r": 159.79837, "b": 220.67920000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 52, "text": "A", "bbox": {"l": 154.3298, "t": 213.57457999999997, "r": 159.79837, "b": 220.67920000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 47, "label": "text", "bbox": {"l": 321.07053, "t": 213.57457999999997, "r": 326.53909, 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On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "headers": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580380082130432, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.907667338848114, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325800538063049, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439422488212585, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325800538063049, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439422488212585, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "body": [{"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439422488212585, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "headers": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325800538063049, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.78900545835495, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443227767944, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290871620178223, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393904209136963, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.78900545835495, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443227767944, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290871620178223, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393904209136963, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "body": [{"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443227767944, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290871620178223, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393904209136963, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "headers": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.78900545835495, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.926919162273407, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696111440658569, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "picture", "bbox": {"l": 137.41452026367188, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387974739074707, "cells": [], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844798445701599, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.926919162273407, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696111440658569, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41452026367188, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387974739074707, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844798445701599, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}], "body": [{"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41452026367188, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387974739074707, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844798445701599, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}], "headers": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.926919162273407, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696111440658569, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919059038162231, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633575081825256, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860696792603, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671324491500854, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556634783744812, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184003472328186, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438936114311218, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636077642440796, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443691968917847, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919059038162231, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633575081825256, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860696792603, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671324491500854, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556634783744812, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184003472328186, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438936114311218, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636077642440796, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443691968917847, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "body": [{"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633575081825256, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860696792603, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671324491500854, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556634783744812, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184003472328186, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438936114311218, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636077642440796, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443691968917847, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "headers": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919059038162231, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 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"text": "L", "bbox": {"l": 307.46613, "t": 244.57372999999995, "r": 312.99161, "b": 253.89550999999994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "L", "bbox": {"l": 318.76886, "t": 244.44037000000003, "r": 324.29434, "b": 253.76215000000002, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "X", "bbox": {"l": 294.9021, "t": 256.70154, "r": 301.03976, "b": 266.02332, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "X X", "bbox": {"l": 307.17743, "t": 256.70154, "r": 325.59039, "b": 266.02332, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "X", "bbox": {"l": 294.78949, "t": 269.25420999999994, "r": 300.92715, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "X X", "bbox": {"l": 307.06482, "t": 269.25420999999994, "r": 325.47778, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "C", "bbox": {"l": 195.93939, "t": 268.74798999999996, "r": 203.11456, "b": 278.06976, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "L", "bbox": {"l": 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334.51135, "t": 242.99463000000003, "r": 337.22485, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "- simple cells: \"C\"", "bbox": {"l": 339.93835, "t": 242.99463000000003, "r": 391.49472, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 334.51135, "t": 252.93255999999997, "r": 337.33313, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "- horizontal merges: \"C\", \"L\"", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "3", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "- vertical merges: \"C\", \"U\"", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "4", "bbox": {"l": 334.51135, "t": 272.80841, "r": 337.30188, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "- 2d merges: \"C\", \"L\", \"U\", \"X\"", "bbox": {"l": 340.09241, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "1", "bbox": {"l": 185.67178, "t": 244.04224, "r": 189.35544, "b": 250.25671, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "2", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "3", "bbox": {"l": 239.34152, "t": 243.62523999999996, "r": 243.02518, "b": 249.83972000000006, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "4", "bbox": {"l": 271.32852, "t": 243.49390000000005, "r": 275.01218, "b": 249.70836999999995, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "2", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "1", "bbox": {"l": 257.24402, "t": 189.961, "r": 260.92767, "b": 196.17548, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 186.87526, "t": 177.97668, "r": 190.55891, "b": 184.19115999999997, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "4", "bbox": {"l": 196.48746, "t": 169.01520000000005, "r": 200.17111, "b": 175.22968000000003, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "A", "bbox": {"l": 169.74728, "t": 167.88225999999997, "r": 175.72659, "b": 175.65039000000002, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "B", "bbox": {"l": 169.74728, "t": 206.83867999999995, "r": 175.72659, "b": 214.60681, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "C", "bbox": {"l": 274.29419, "t": 168.27972, "r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "D", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "E", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326505661010742, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749722838401794, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, 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463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, 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First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590607285499573, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326505661010742, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749722838401794, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}, {"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 3. 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Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506187319755554, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247231602668762, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259926080703735, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420595765113831, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785566329956055, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590607285499573, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "body": [{"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding"}, {"label": "picture", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "picture", "bbox": {"l": 164.6502227783203, "t": 163.79708862304688, "r": 449.55072021484375, "b": 280.3410339355469, "coord_origin": "TOPLEFT"}, "confidence": 0.7868288159370422, "cells": [], "children": [{"id": 77, "label": "text", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 66, "text": "2", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 435.16009999999994, "t": 167.69011999999998, "r": 447.86273, "b": 177.01189999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.0, 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Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506187319755554, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247231602668762, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259926080703735, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420595765113831, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785566329956055, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590607285499573, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "headers": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326505661010742, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749722838401794, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.", "bbox": {"l": 147.30025, "t": 540.73164, "r": 149.70605, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Item", "bbox": {"l": 150.90895, "t": 540.73164, "r": 155.72055, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Amount", "bbox": {"l": 162.75987, "t": 535.3938, "r": 172.2963, "b": 537.76224, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Names", "bbox": {"l": 147.63603, "t": 535.3661500000001, "r": 155.91753, "b": 537.73459, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1000", "bbox": {"l": 158.48466, "t": 540.73164, "r": 164.10178, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "500", "bbox": {"l": 158.48466, "t": 544.67065, "r": 162.69737, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "3500", "bbox": {"l": 158.48466, "t": 548.91264, "r": 164.10178, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "150", "bbox": {"l": 158.48466, "t": 553.15465, "r": 162.69737, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "unit", "bbox": {"l": 168.81696, "t": 540.73164, "r": 172.88876, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "unit", "bbox": {"l": 168.81696, "t": 544.67065, "r": 172.88876, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "unit", "bbox": {"l": 168.81696, "t": 548.91264, "r": 172.88876, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "unit", "bbox": {"l": 168.81696, "t": 553.15465, "r": 172.88876, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "2.", "bbox": {"l": 147.30025, "t": 544.67065, "r": 149.70605, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Item", "bbox": {"l": 150.90895, "t": 544.67065, "r": 155.72055, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "3.", "bbox": {"l": 147.30025, "t": 548.91264, "r": 149.70605, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Item", "bbox": {"l": 150.90895, "t": 548.91264, "r": 155.72055, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "4.", "bbox": {"l": 147.30025, "t": 553.15465, "r": 149.70605, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Item", "bbox": {"l": 150.90895, "t": 553.15465, "r": 155.72055, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Extracted", "bbox": {"l": 152.05046, "t": 517.0098, "r": 171.24945, "b": 521.27298, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Table Images", "bbox": {"l": 148.13347, "t": 522.3122900000001, "r": 175.16759, "b": 526.57547, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Standardized", "bbox": {"l": 193.53331, "t": 524.51422, "r": 220.31973, "b": 528.7774, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Images", "bbox": {"l": 199.47311, "t": 529.8167100000001, "r": 214.37889, "b": 534.0799, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "BBox", "bbox": {"l": 273.61066, "t": 509.9053, "r": 284.47275, "b": 514.16849, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Decoder", "bbox": {"l": 270.45187, "t": 513.6928399999999, "r": 287.63242, "b": 517.9560200000001, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "BBoxes", "bbox": {"l": 332.47852, "t": 508.14438, "r": 348.14014, "b": 512.40756, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "BBoxes can be", "bbox": {"l": 376.68622, "t": 521.12024, "r": 407.25497, "b": 525.38342, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "traced back to the", "bbox": {"l": 373.90869, "t": 525.66525, "r": 410.03506, "b": 529.92844, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "original image to", "bbox": {"l": 375.29871, "t": 530.21024, "r": 408.64902, "b": 534.47342, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "extract content", "bbox": {"l": 377.06747, "t": 534.75522, "r": 406.88312, "b": 539.01843, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Structure Tags sequence", "bbox": {"l": 383.56683, "t": 563.24176, "r": 433.76544, "b": 567.50497, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "provide full description of", "bbox": {"l": 383.52768, "t": 567.78676, "r": 433.80764999999997, "b": 572.04997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "the table structure", "bbox": {"l": 390.47522, "t": 572.33177, "r": 426.85703, "b": 576.59499, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Structure Tags", "bbox": {"l": 293.94702, "t": 577.89143, "r": 323.1691, "b": 582.15465, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "in OTSL format", "bbox": {"l": 293.94702, "t": 582.43648, "r": 324.59396, "b": 586.69969, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "BBoxes in sync", "bbox": {"l": 333.07819, "t": 541.82269, "r": 364.14691, "b": 546.08591, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "with tag sequence", "bbox": {"l": 333.07819, "t": 545.6102, "r": 369.71542, "b": 549.87341, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Encoder", "bbox": {"l": 232.65881000000002, "t": 515.24139, "r": 249.58894000000004, "b": 519.50458, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "Structure", "bbox": {"l": 269.8219, "t": 545.97102, "r": 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the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301324844360352, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8631827235221863, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464746713638306, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515273571014404, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297955632209778, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 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664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9512704014778137, "cells": [{"id": 105, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.58792, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.58871, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301324844360352, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8"}, {"label": "page_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8631827235221863, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464746713638306, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515273571014404, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297955632209778, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.7096710205078, "t": 508.06390380859375, "r": 472.73382568359375, "b": 593.67724609375, "coord_origin": "TOPLEFT"}, "confidence": 0.9303393959999084, "cells": [], "children": [{"id": 34, "label": "text", "bbox": {"l": 332.47852, "t": 508.14438, "r": 348.14014, "b": 512.40756, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 57, "text": "BBoxes", "bbox": {"l": 332.47852, "t": 508.14438, "r": 348.14014, "b": 512.40756, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 273.61066, "t": 509.9053, "r": 284.47275, "b": 514.16849, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 55, "text": "BBox", "bbox": {"l": 273.61066, "t": 509.9053, "r": 284.47275, "b": 514.16849, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, 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The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}], "body": [{"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464746713638306, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515273571014404, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297955632209778, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 4. 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The predicted OTSL structures were converted back to HTML format in"}], "headers": [{"label": "page_header", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301324844360352, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8"}, {"label": "page_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8631827235221863, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 231.43106, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Language", "bbox": {"l": 239.79799999999997, "t": 347.21396, "r": 278.31766, "b": 355.28372, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TEDs", "bbox": {"l": 324.67001, "t": 341.73495, "r": 348.26419, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "mAP", "bbox": {"l": 396.271, "t": 341.73495, "r": 417.12683, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "(0.75)", "bbox": {"l": 394.927, "t": 352.69394000000005, "r": 418.47278, "b": 360.7637, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Inference", "bbox": {"l": 430.771, "t": 341.73495, "r": 467.1423, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "time (secs)", "bbox": {"l": 427.14801, "t": 352.69394000000005, "r": 470.76056, "b": 360.7637, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "simple", "bbox": {"l": 286.686, "t": 354.68594, "r": 312.33261, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "complex", "bbox": {"l": 320.702, "t": 354.68594, "r": 353.71988, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "all", "bbox": {"l": 369.306, "t": 354.68594, "r": 379.03094, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "6", "bbox": {"l": 161.90601, "t": 373.51596, "r": 166.51294, "b": 381.58572, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "6", "bbox": {"l": 209.509, "t": 373.51596, "r": 214.11594, "b": 381.58572, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 368.03595, "r": 271.40527, "b": 376.10571, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "0.965", "bbox": {"l": 289.017, "t": 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396.20599, "t": 407.28894, "r": 417.19275, "b": 415.3587, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3.77", "bbox": {"l": 440.767, "t": 407.28894, "r": 457.14682, "b": 415.3587, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "2", "bbox": {"l": 161.90601, "t": 426.11795, "r": 166.51294, "b": 434.1877099999999, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "4", "bbox": {"l": 209.509, "t": 426.11795, "r": 214.11594, "b": 434.1877099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 420.63895, "r": 271.40527, "b": 428.70871, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "0.923", "bbox": {"l": 289.017, "t": 420.63895, "r": 310.00375, "b": 428.70871, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0.897", "bbox": {"l": 326.71701, "t": 420.63895, "r": 347.70377, "b": 428.70871, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "0.915", "bbox": {"l": 363.67599, "t": 420.63895, "r": 384.66275, "b": 428.70871, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "0.859", "bbox": {"l": 394.61801, "t": 420.57617, "r": 418.77887, "b": 428.50247, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "1.91", "bbox": {"l": 439.52701, "t": 420.57617, "r": 458.38425, "b": 428.50247, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 433.58994, "r": 272.93954, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "0.945", "bbox": {"l": 289.017, "t": 433.58994, "r": 310.00375, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "0.901", "bbox": {"l": 326.71701, "t": 433.58994, "r": 347.70377, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "0.931", "bbox": {"l": 362.08801, "t": 433.5271599999999, "r": 386.24887, "b": 441.45346, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "0.834", "bbox": {"l": 396.20599, "t": 433.58994, "r": 417.19275, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "3.81", "bbox": {"l": 440.767, "t": 433.58994, "r": 457.14682, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "4", "bbox": {"l": 161.90601, "t": 452.41995, "r": 166.51294, "b": 460.48972, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "2", "bbox": {"l": 209.509, "t": 452.41995, "r": 214.11594, "b": 460.48972, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 446.9399399999999, "r": 271.40527, "b": 455.0097, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "0.952", "bbox": {"l": 289.017, "t": 446.9399399999999, "r": 310.00375, "b": 455.0097, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "0.92", "bbox": {"l": 329.021, "t": 446.9399399999999, "r": 345.40082, "b": 455.0097, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "0.942", "bbox": {"l": 362.08801, "t": 446.87717, "r": 386.24887, "b": 454.80347, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "0.857", "bbox": {"l": 394.61801, "t": 446.87717, "r": 418.77887, "b": 454.80347, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1.22", "bbox": {"l": 439.52701, "t": 446.87717, "r": 458.38425, "b": 454.80347, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 459.8919399999999, "r": 272.93954, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "0.944", "bbox": {"l": 289.017, "t": 459.8919399999999, "r": 310.00375, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0.903", "bbox": {"l": 326.71701, "t": 459.8919399999999, "r": 347.70377, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "0.931", "bbox": {"l": 363.67599, "t": 459.8919399999999, "r": 384.66275, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0.824", "bbox": {"l": 396.20599, "t": 459.8919399999999, "r": 417.19275, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "2", "bbox": {"l": 446.65302, "t": 459.8919399999999, "r": 451.25995, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 508.15179, "r": 149.40205, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378157615662, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8857628107070923, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59579, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579682350158691, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.957078218460083, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747375488281, "r": 475.00372314453125, "b": 469.4720764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": 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Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579682350158691, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.957078218460083, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747375488281, "r": 475.00372314453125, "b": 469.4720764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 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458.38425, "b": 467.9617, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}, "confidence": 0.9592539668083191, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 508.15179, "r": 149.40205, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.5957599999999, "b": 617.03474, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5957599999999, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59579, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579682350158691, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.957078218460083, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747375488281, "r": 475.00372314453125, "b": 469.4720764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 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617.03474, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5957599999999, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378157615662, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8857628107070923, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9"}]}}, {"page_no": 9, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "10", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 2.", "bbox": {"l": 134.765, "t": 115.83618000000001, "r": 173.09366, "b": 123.76251000000002, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "TSR and cell detection results compared between OTSL and HTML on", "bbox": {"l": 181.30299, "t": 115.89899000000003, "r": 480.59151999999995, "b": 123.96868999999992, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using Table-", "bbox": {"l": 134.765, "t": 126.85797000000014, "r": 480.59357000000006, "b": 134.92767000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Former [9] (with enc=6, dec=6, heads=8).", "bbox": {"l": 134.765, "t": 137.81696, "r": 305.95691, "b": 145.88666, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Data set", "bbox": {"l": 160.782, "t": 166.55895999999996, "r": 194.99779, "b": 174.62865999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Language", "bbox": {"l": 215.52499000000003, "t": 166.534, "r": 254.04465, "b": 174.6037, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "TEDs", "bbox": {"l": 300.397, "t": 161.07898, "r": 323.99118, "b": 169.14868, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "mAP(0.75)", "bbox": {"l": 370.345, "t": 166.55895999999996, "r": 414.74661, "b": 174.62865999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Inference", "bbox": {"l": 426.737, "t": 161.07898, "r": 463.10830999999996, "b": 169.14868, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "time (secs)", "bbox": {"l": 423.11401, "t": 172.03796, "r": 466.72656, "b": 180.10766999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "simple", "bbox": {"l": 262.41299, "t": 174.03101000000004, "r": 288.0596, "b": 182.10071000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "complex", "bbox": {"l": 296.42899, "t": 174.03101000000004, "r": 329.44687, "b": 182.10071000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "all", "bbox": {"l": 345.03299, "t": 174.03101000000004, "r": 354.75793, "b": 182.10071000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "PubTabNet", "bbox": {"l": 154.53799, "t": 192.85999000000004, "r": 201.24129, "b": 200.92969000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "OTSL", "bbox": {"l": 222.43700000000004, "t": 187.38098000000002, "r": 247.13226000000003, "b": 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"0.922", "bbox": {"l": 302.444, "t": 226.63396999999998, "r": 323.43076, "b": 234.70367, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "0.92", "bbox": {"l": 341.70599, "t": 226.63396999999998, "r": 358.08582, "b": 234.70367, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "0.722", "bbox": {"l": 382.052, "t": 226.63396999999998, "r": 403.03876, "b": 234.70367, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "3.26", "bbox": {"l": 436.73199000000005, "t": 226.63396999999998, "r": 453.11182, "b": 234.70367, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "PubTables-1M", "bbox": {"l": 148.62601, "t": 245.46294999999998, "r": 207.1524, "b": 253.53265, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "OTSL", "bbox": {"l": 222.43700000000004, "t": 239.98297000000002, "r": 247.13226000000003, "b": 248.05267000000003, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "0.987", "bbox": {"l": 264.74399, "t": 239.98297000000002, "r": 285.73074, "b": 248.05267000000003, "coord_origin": "TOPLEFT"}}, 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"TOPLEFT"}}, {"id": 52, "text": "0.966", "bbox": {"l": 339.40302, "t": 252.93499999999995, "r": 360.38977, "b": 261.00469999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "0.889", "bbox": {"l": 382.052, "t": 252.93499999999995, "r": 403.03876, "b": 261.00469999999996, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "3.26", "bbox": {"l": 436.73199000000005, "t": 252.93499999999995, "r": 453.11182, "b": 261.00469999999996, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "5.3", "bbox": {"l": 134.765, "t": 288.91479, "r": 149.40205, "b": 297.72173999999995, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Qualitative Results", "bbox": {"l": 160.85904, "t": 288.91479, "r": 257.08679, "b": 297.72173999999995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5", "bbox": {"l": 134.765, "t": 309.86078, "r": 480.58777, "b": 318.65775, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "demonstrates less overlap and more accurate bounding boxes with OTSL. In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap (E) than the HTML model (D), when predicting the structure of a sparse ta-", "bbox": {"l": 134.765, "t": 408.61190999999997, "r": 480.59106, "b": 416.68167000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "ble (A), at twice the inference speed because of shorter sequence length (B),(C).", "bbox": {"l": 134.765, "t": 419.57089, "r": 480.58838000000003, "b": 427.64066, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\"PMC2807444_006_00.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 430.52987999999993, "r": 304.69171, "b": 438.59964, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "<table>", "bbox": {"l": 180.12473, "t": 516.2332200000001, "r": 190.62042, "b": 518.94992, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "<tr><td></td><td colspan=\"4\"></td><td colspan=\"6\"></td><td colspan=\"3\"></td></tr>", "bbox": {"l": 183.2438, "t": 520.13208, "r": 304.54797, "b": 522.84879, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>", "bbox": {"l": 183.2438, "t": 524.03094, "r": 388.42313, "b": 526.74765, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>", "bbox": {"l": 183.2438, "t": 527.9297799999999, "r": 388.42313, "b": 530.64648, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>", "bbox": {"l": 183.2438, "t": 531.82861, "r": 388.42313, "b": 534.54532, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>", "bbox": {"l": 183.2438, "t": 535.72748, "r": 388.42313, "b": 538.44418, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>", "bbox": {"l": 183.2438, "t": 539.62631, "r": 388.42313, "b": 542.34303, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>", "bbox": {"l": 183.2438, "t": 543.52516, "r": 388.42313, "b": 546.24188, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>", "bbox": {"l": 183.2438, "t": 547.42401, "r": 388.42313, "b": 550.14073, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>", "bbox": {"l": 183.2438, "t": 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bounding boxes with OTSL. In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes."}, {"label": "caption", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 397.59012, "r": 480.59106, "b": 439.71716, "coord_origin": "TOPLEFT"}, "confidence": 0.9482712745666504, "cells": [{"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap (E) than the HTML model (D), when predicting the structure of a sparse ta-", "bbox": {"l": 134.765, "t": 408.61190999999997, "r": 480.59106, "b": 416.68167000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "ble (A), at twice the inference speed because of shorter sequence length (B),(C).", "bbox": {"l": 134.765, "t": 419.57089, "r": 480.58838000000003, "b": 427.64066, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\"PMC2807444_006_00.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 430.52987999999993, "r": 304.69171, "b": 438.59964, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "\u03bc", "bbox": {"l": 342.63354, "t": 430.19678, "r": 344.81915, "b": 439.71716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 5. 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The OTSL model (B) captured repeating pattern of horizontally", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.58823, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 480.5881999999999, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and", "bbox": {"l": 134.765, "t": 158.73895000000005, "r": 480.58838000000003, "b": 166.80864999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 169.69794000000002, "r": 415.84454, "b": 177.76764000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"label": "picture", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 168.39263916015625, "t": 181.96795654296875, "r": 447.35272216796875, "b": 634.003173828125, "coord_origin": "TOPLEFT"}, "confidence": 0.7615750432014465, "cells": [], "children": [{"id": 10, "label": "text", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 15, "text": "A", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 181.89114, "t": 288.35962000000006, "r": 239.23492, "b": 294.2947700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 13, "text": "Repeating pattern of", "bbox": {"l": 181.89114, "t": 288.35962000000006, "r": 239.23492, "b": 294.2947700000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 181.89114, "t": 294.89423, "r": 251.52917, "b": 300.82938, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 14, "text": "horizontally merged cells", "bbox": {"l": 181.89114, "t": 294.89423, "r": 251.52917, "b": 300.82938, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 8, "text": "B", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 172.27777, "t": 381.36288, "r": 180.18666, "b": 403.40067, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 17, "text": "OTSL", "bbox": {"l": 172.27777, "t": 381.36288, "r": 180.18666, "b": 403.40067, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 293.64209, "t": 465.59784, "r": 437.50800000000004, "b": 471.53299, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 12, "text": "Repeating pattern is well represented in predictions", "bbox": {"l": 293.64209, "t": 465.59784, "r": 437.50800000000004, "b": 471.53299, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 9, "text": "C", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 172.27747, "t": 555.7769499999999, "r": 180.18663, "b": 578.7478, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 18, "text": "HTML", "bbox": {"l": 172.27747, "t": 555.7769499999999, "r": 180.18663, "b": 578.7478, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 292.18976, "t": 607.80609, "r": 381.54663, "b": 613.7412400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 16, "text": "Bounding box drifting at the end", "bbox": {"l": 292.18976, "t": 607.80609, "r": 381.54663, "b": 613.7412400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 11, "text": "Horizontally merged cells are not present", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 10, "text": "Incorrect end of HTML sequence", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "caption", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.58838000000003, "b": 177.76764000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8657404184341431, "cells": [{"id": 2, "text": "Fig. 6.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Visualization of predicted structure and detected bounding boxes on a complex", "bbox": {"l": 165.215, "t": 125.86200000000008, "r": 480.58752, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.58823, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 480.5881999999999, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and", "bbox": {"l": 134.765, "t": 158.73895000000005, "r": 480.58838000000003, "b": 166.80864999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 169.69794000000002, "r": 415.84454, "b": 177.76764000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"label": "picture", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 168.39263916015625, "t": 181.96795654296875, "r": 447.35272216796875, "b": 634.003173828125, "coord_origin": "TOPLEFT"}, "confidence": 0.7615750432014465, "cells": [], "children": [{"id": 10, "label": "text", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 15, "text": "A", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 181.89114, "t": 288.35962000000006, "r": 239.23492, "b": 294.2947700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 13, "text": "Repeating 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"t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9301635026931763, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "page_header", "bbox": {"l": 471.37561, "t": 93.77099999999996, "r": 480.5894799999999, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9007187485694885, "cells": [{"id": 1, "text": "11", "bbox": {"l": 471.37561, "t": 93.77099999999996, "r": 480.5894799999999, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8610868453979492, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8927640914916992, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156443476676941, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806177020072937, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868756532669067, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403368830680847, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698705077171326, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 139.37097, "t": 567.51884, "r": 480.5920100000001, "b": 608.46561, "coord_origin": "TOPLEFT"}, "confidence": 0.973068118095398, "cells": [{"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 480.58731000000006, "b": 631.18761, "coord_origin": "TOPLEFT"}, "confidence": 0.9617277979850769, "cells": [{"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 139.37097, "t": 634.88084, "r": 480.58826, "b": 664.86761, "coord_origin": "TOPLEFT"}, "confidence": 0.975471019744873, "cells": [{"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8610868453979492, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}, {"label": "page_header", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8927640914916992, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156443476676941, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806177020072937, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868756532669067, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403368830680847, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698705077171326, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. 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IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)"}], "body": [{"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156443476676941, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806177020072937, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868756532669067, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. 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Pattern Recognition", "bbox": {"l": 151.51797, "t": 207.34491000000003, "r": 318.55124, "b": 215.41461000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "126", "bbox": {"l": 321.62097, "t": 207.2821, "r": 337.53186, "b": 215.20844, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ", 108565 (2022)", "bbox": {"l": 337.53296, "t": 207.34491000000003, "r": 399.46927, "b": 215.41461000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "20. Zhang, Z., Zhang, J., Du, J., Wang, F.: Split, embed and merge: An accurate table structure recognizer. Pattern Recognition 126 , 108565 (2022)"}, {"label": "list_item", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 134.76495, "t": 218.30389000000002, "r": 480.59305000000006, "b": 270.25438999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9720271825790405, "cells": [{"id": 19, "text": "21.", "bbox": {"l": 134.76495, "t": 218.30389000000002, "r": 145.7213, "b": 226.3736, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Zheng, X., Burdick, D., Popa, L., Zhong, X., Wang, N.X.R.: Global table extractor", "bbox": {"l": 150.00871, "t": 218.30389000000002, "r": 480.59012, "b": 226.3736, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "(gte): A framework for joint table identification and cell structure recognition using", "bbox": {"l": 151.51796, "t": 229.26288, "r": 480.59102999999993, "b": 237.33258, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "visual context. In: 2021 IEEE Winter Conference on Applications of Computer Vi-", "bbox": {"l": 151.51796, "t": 240.22186, "r": 480.59119, "b": 248.29156, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "sion (WACV). pp. 697-706 (2021).", "bbox": {"l": 151.51796, "t": 251.18084999999996, "r": 293.44086, "b": 259.25055, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "https://doi.org/10.1109/WACV48630.2021.", "bbox": {"l": 297.04996, "t": 251.82641999999998, "r": 480.59305000000006, "b": 259.29540999999995, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "00074", "bbox": {"l": 151.51796, "t": 262.7854, "r": 175.05028, "b": 270.25438999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "21. Zheng, X., Burdick, D., Popa, L., Zhong, X., Wang, N.X.R.: Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 697-706 (2021). https://doi.org/10.1109/WACV48630.2021. 00074"}, {"label": "list_item", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 134.76495, "t": 273.09882000000005, "r": 480.59558, "b": 314.0455, "coord_origin": "TOPLEFT"}, "confidence": 0.9685125946998596, "cells": [{"id": 26, "text": "22.", "bbox": {"l": 134.76495, "t": 273.09882000000005, "r": 146.36798, "b": 281.16855000000004, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition:", "bbox": {"l": 150.90846, "t": 273.09882000000005, "r": 480.59094, "b": 281.16855000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Data, model, and evaluation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M.", "bbox": {"l": 151.51796, "t": 284.05777, "r": 480.58832000000007, "b": 292.12753, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "(eds.) Computer Vision - ECCV 2020. pp. 564-580. Springer International Pub-", "bbox": {"l": 151.51796, "t": 295.01675, "r": 480.59558, "b": 303.08651999999995, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "lishing, Cham (2020)", "bbox": {"l": 151.51796, "t": 305.97574, "r": 236.02359, "b": 314.0455, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "22. Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition: Data, model, and evaluation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision - ECCV 2020. pp. 564-580. Springer International Publishing, Cham (2020)"}, {"label": "list_item", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 134.76495, "t": 316.93472, "r": 480.59454, "b": 346.92148, "coord_origin": "TOPLEFT"}, "confidence": 0.9668972492218018, "cells": [{"id": 31, "text": "23.", "bbox": {"l": 134.76495, "t": 316.93472, "r": 145.69547, "b": 325.00449000000003, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document lay-", "bbox": {"l": 149.97276, "t": 316.93472, "r": 480.59454, "b": 325.00449000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "out analysis. In: 2019 International Conference on Document Analysis and Recog-", "bbox": {"l": 151.51796, "t": 327.8927299999999, "r": 480.59387000000004, "b": 335.96249, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "nition (ICDAR). pp. 1015-1022. IEEE (2019)", "bbox": {"l": 151.51796, "t": 338.85172, "r": 335.13635, "b": 346.92148, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "23. Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1015-1022. IEEE (2019)"}], "headers": [{"label": "page_header", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7012730240821838, "cells": [{"id": 0, "text": "14", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "14"}, {"label": "page_header", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7889755368232727, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}]
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/redp5110_sampled.doctags.txt b/tests/data/groundtruth/docling_v2/redp5110_sampled.doctags.txt
index 84485382..a1c69510 100644
--- a/tests/data/groundtruth/docling_v2/redp5110_sampled.doctags.txt
+++ b/tests/data/groundtruth/docling_v2/redp5110_sampled.doctags.txt
@@ -12,7 +12,7 @@
 </figure>
 <section_header_level_1><location><page_2><loc_11><loc_88><loc_28><loc_91></location>Contents</section_header_level_1>
 <table>
-<location><page_2><loc_22><loc_10><loc_90><loc_83></location>
+<location><page_2><loc_22><loc_10><loc_89><loc_83></location>
 <row_0><col_0><body>Notices</col_0><col_1><body>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii</col_1></row_0>
 <row_1><col_0><body>Trademarks</col_0><col_1><body>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii</col_1></row_1>
 <row_2><col_0><body>DB2 for i Center of Excellence</col_0><col_1><body>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix</col_1></row_2>
@@ -46,8 +46,8 @@
 <row_30><col_0><body>3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .</col_0><col_1><body>19</col_1></row_30>
 <row_31><col_0><body>3.3 VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .</col_0><col_1><body>20</col_1></row_31>
 <row_32><col_0><body>3.4 Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .</col_0><col_1><body>21</col_1></row_32>
-<row_33><col_0><body>3.5 SELECT, INSERT, and UPDATE behavior with RCAC</col_0><col_1><body>. . . . . . . . . . . . . . . . . . . . . . . . 22</col_1></row_33>
-<row_34><col_0><body>3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .</col_0><col_1><body>22</col_1></row_34>
+<row_33><col_0><body>. . . . . . . . . . . . . . . . . . . . . . . .</col_0><col_1><body>22</col_1></row_33>
+<row_34><col_0><body>3.5 SELECT, INSERT, and UPDATE behavior with RCAC 3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .</col_0><col_1><body>22</col_1></row_34>
 <row_35><col_0><body>3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .</col_0><col_1><body>23</col_1></row_35>
 <row_36><col_0><body>3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .</col_0><col_1><body>23</col_1></row_36>
 <row_37><col_0><body>3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .</col_0><col_1><body>24</col_1></row_37>
@@ -155,7 +155,7 @@
 <section_header_level_1><location><page_8><loc_11><loc_71><loc_89><loc_72></location>2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view</section_header_level_1>
 <text><location><page_8><loc_22><loc_66><loc_85><loc_69></location>The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view.</text>
 <table>
-<location><page_8><loc_23><loc_45><loc_88><loc_63></location>
+<location><page_8><loc_22><loc_44><loc_89><loc_63></location>
 <caption>Table 2-1 FUNCTION_USAGE view</caption>
 <row_0><col_0><col_header>Column name</col_0><col_1><col_header>Data type</col_1><col_2><col_header>Description</col_2></row_0>
 <row_1><col_0><body>FUNCTION_ID</col_0><col_1><body>VARCHAR(30)</col_1><col_2><body>ID of the function.</col_2></row_1>
@@ -185,21 +185,21 @@
 <text><location><page_9><loc_22><loc_57><loc_88><loc_63></location>A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself.</text>
 <text><location><page_9><loc_22><loc_53><loc_89><loc_56></location>Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools.</text>
 <table>
-<location><page_9><loc_12><loc_10><loc_88><loc_49></location>
+<location><page_9><loc_11><loc_9><loc_89><loc_50></location>
 <caption>Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority</caption>
-<row_0><col_0><body>User action</col_0><col_1><body>*JOBCTL</col_1><col_2><body>QIBM_DB_SECADM</col_2><col_3><body>QIBM_DB_SQLADM</col_3><col_4><body>QIBM_DB_SYSMON No Authority</col_4></row_0>
-<row_1><col_0><row_header>SET CURRENT DEGREE  (SQL statement)</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_1>
-<row_2><col_0><row_header>CHGQRYA  command targeting a different user's job</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_2>
-<row_3><col_0><row_header>STRDBMON  or  ENDDBMON  commands targeting a different user's job</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_3>
-<row_4><col_0><row_header>STRDBMON  or  ENDDBMON  commands targeting a job that matches the current user</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body>X X</col_4></row_4>
-<row_5><col_0><row_header>QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body>X</col_4></row_5>
-<row_6><col_0><row_header>Visual Explain within Run SQL scripts</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body>X X</col_4></row_6>
-<row_7><col_0><row_header>Visual Explain outside of Run SQL scripts</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_7>
-<row_8><col_0><row_header>ANALYZE PLAN CACHE procedure</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_8>
-<row_9><col_0><row_header>DUMP PLAN CACHE procedure</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_9>
-<row_10><col_0><row_header>MODIFY PLAN CACHE procedure</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_10>
-<row_11><col_0><row_header>MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority)</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_11>
-<row_12><col_0><row_header>CHANGE PLAN CACHE SIZE procedure (currently does not check authority)</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4></row_12>
+<row_0><col_0><row_header>User action</col_0><col_1><body>*JOBCTL</col_1><col_2><body>QIBM_DB_SECADM</col_2><col_3><body>QIBM_DB_SQLADM</col_3><col_4><body>QIBM_DB_SYSMON</col_4><col_5><body>No Authority</col_5></row_0>
+<row_1><col_0><row_header>SET CURRENT DEGREE  (SQL statement)</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_1>
+<row_2><col_0><row_header>CHGQRYA  command targeting a different user's job</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_2>
+<row_3><col_0><row_header>STRDBMON  or  ENDDBMON  commands targeting a different user's job</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_3>
+<row_4><col_0><row_header>STRDBMON  or  ENDDBMON  commands targeting a job that matches the current user</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body>X</col_4><col_5><body>X</col_5></row_4>
+<row_5><col_0><row_header>QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body>X</col_4><col_5><body></col_5></row_5>
+<row_6><col_0><row_header>Visual Explain within Run SQL scripts</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body>X</col_4><col_5><body>X</col_5></row_6>
+<row_7><col_0><row_header>Visual Explain outside of Run SQL scripts</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_7>
+<row_8><col_0><row_header>ANALYZE PLAN CACHE procedure</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_8>
+<row_9><col_0><row_header>DUMP PLAN CACHE procedure</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_9>
+<row_10><col_0><row_header>MODIFY PLAN CACHE procedure</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_10>
+<row_11><col_0><row_header>MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority)</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_11>
+<row_12><col_0><row_header>CHANGE PLAN CACHE SIZE procedure (currently does not check authority)</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_12>
 </table>
 <figure>
 <location><page_10><loc_22><loc_48><loc_89><loc_86></location>
@@ -209,7 +209,7 @@
 <text><location><page_10><loc_22><loc_37><loc_89><loc_43></location>A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number.</text>
 <paragraph><location><page_11><loc_22><loc_90><loc_67><loc_91></location>Table 3-1 summarizes these special registers and their values.</paragraph>
 <table>
-<location><page_11><loc_23><loc_75><loc_88><loc_86></location>
+<location><page_11><loc_22><loc_74><loc_89><loc_87></location>
 <caption>Table 3-1 Special registers and their corresponding values</caption>
 <row_0><col_0><col_header>Special register</col_0><col_1><col_header>Corresponding value</col_1></row_0>
 <row_1><col_0><body>USER or SESSION_USER</col_0><col_1><body>The effective user of the thread excluding adopted authority.</col_1></row_1>
@@ -233,7 +233,7 @@
 <text><location><page_11><loc_22><loc_9><loc_87><loc_13></location>IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic.</text>
 <text><location><page_12><loc_22><loc_90><loc_56><loc_91></location>Table 3-2 lists the nine built-in global variables.</text>
 <table>
-<location><page_12><loc_12><loc_63><loc_86><loc_86></location>
+<location><page_12><loc_10><loc_63><loc_90><loc_87></location>
 <caption>Table 3-2 Built-in global variables</caption>
 <row_0><col_0><col_header>Global variable</col_0><col_1><col_header>Type</col_1><col_2><col_header>Description</col_2></row_0>
 <row_1><col_0><body>CLIENT_HOST</col_0><col_1><body>VARCHAR(255)</col_1><col_2><body>Host name of the current client as returned by the system</col_2></row_1>
diff --git a/tests/data/groundtruth/docling_v2/redp5110_sampled.json b/tests/data/groundtruth/docling_v2/redp5110_sampled.json
index 8ca15d64..f99e4508 100644
--- a/tests/data/groundtruth/docling_v2/redp5110_sampled.json
+++ b/tests/data/groundtruth/docling_v2/redp5110_sampled.json
@@ -1 +1 @@
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64.80000305175781, "t": 718.1519775390625, "r": 151.46160888671875, "b": 695.9519653320312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Preface", "text": "Preface", "level": 1}, {"self_ref": "#/texts/37", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.79983520507812, "t": 659.3513793945312, "r": 547.3082275390625, "b": 590.1392822265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 469]}], "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"self_ref": "#/texts/38", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.79986572265625, "t": 577.3925170898438, "r": 546.4656982421875, "b": 532.1800537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"self_ref": "#/texts/39", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.8000030517578, "t": 471.37127685546875, "r": 547.2366943359375, "b": 450.1584777832031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 172]}], "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"self_ref": "#/texts/40", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 263.3995666503906, "t": 416.3512268066406, "r": 541.2507934570312, "b": 275.1402587890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 684]}], "orig": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office.", "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"self_ref": "#/texts/41", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 64.80000305175781, "t": 36.461997985839844, "r": 257.24334716796875, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"self_ref": "#/texts/42", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 263.39959716796875, "t": 264.37347412109375, "r": 541.2737426757812, "b": 111.162841796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 726]}], "orig": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com .", "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"self_ref": "#/texts/43", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 538.8599853515625, "t": 37.15127944946289, "r": 547.2503051757812, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "xi", "text": "xi"}, {"self_ref": "#/texts/44", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 64.80000305175781, "t": 503.69940185546875, "r": 125.36660766601562, "b": 488.9364013671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Authors", "text": "Authors", "level": 1}, {"self_ref": "#/texts/45", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 81.0, "t": 523.457275390625, "r": 115.13253021240234, "b": 517.019287109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Chapter 1.", "text": "Chapter 1."}, {"self_ref": "#/texts/46", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 500.3999938964844, "t": 698.831298828125, "r": 522.6177368164062, "b": 661.8682861328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/47", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 537.1136474609375, "r": 547.3047485351562, "b": 482.1217956542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "Securing and protecting IBM DB2 data", "text": "Securing and protecting IBM DB2 data", "level": 1}, {"self_ref": "#/texts/48", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.79965209960938, "t": 443.2912902832031, "r": 547.2540283203125, "b": 362.078857421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 648]}], "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record.", "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"self_ref": "#/texts/49", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.80023193359375, "t": 349.27227783203125, "r": 527.206298828125, "b": 304.0598449707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 304]}], "orig": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement.", "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"self_ref": "#/texts/50", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 291.3130187988281, "r": 547.1551513671875, "b": 270.1002197265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 122]}], "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:", "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:"}, {"self_ref": "#/texts/51", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 262.2736511230469, "r": 250.23167419433594, "b": 253.06063842773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "GLYPH<SM590000> Security fundamentals", "text": "GLYPH<SM590000> Security fundamentals", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/52", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 250.27383422851562, "r": 282.98114013671875, "b": 241.0608367919922, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "GLYPH<SM590000> Current state of IBM i security", "text": "GLYPH<SM590000> Current state of IBM i security", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/53", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 238.27403259277344, "r": 264.8818664550781, "b": 229.06103515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "GLYPH<SM590000> DB2 for i security controls", "text": "GLYPH<SM590000> DB2 for i security controls", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/54", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 74.24993896484375, "r": 258.362548828125, "b": 67.21955871582031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "$^{1 }$http://www.idtheftcenter.org", "text": "$^{1 }$http://www.idtheftcenter.org"}, {"self_ref": "#/texts/55", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 64.40973663330078, "r": 234.05880737304688, "b": 57.02824020385742, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "$^{2 }$http://www.ponemon.org /", "text": "$^{2 }$http://www.ponemon.org /"}, {"self_ref": "#/texts/56", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 64.80000305175781, "t": 36.461997985839844, "r": 257.24334716796875, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"self_ref": "#/texts/57", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 541.6798706054688, "t": 37.15127944946289, "r": 547.2176513671875, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 717.6593017578125, "r": 267.40582275390625, "b": 702.8963012695312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "1.1 Security fundamentals", "text": "1.1 Security fundamentals", "level": 1}, {"self_ref": "#/texts/59", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 685.3912963867188, "r": 545.0048217773438, "b": 664.178466796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 133]}], "orig": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:", "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"self_ref": "#/texts/60", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 656.8751220703125, "r": 547.1642456054688, "b": 611.138916015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 361]}], "orig": "GLYPH<SM590000> First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "text": "GLYPH<SM590000> First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/61", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 151.199462890625, "t": 603.3721313476562, "r": 547.2608642578125, "b": 522.1602172851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 587]}], "orig": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 151.199462890625, "t": 514.3934326171875, "r": 541.9920043945312, "b": 505.180419921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "A security policy is what defines whether the system and its settings are secure (or not).", "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"self_ref": "#/texts/63", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 136.79930114746094, "t": 497.8750305175781, "r": 547.1582641601562, "b": 416.139404296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8002166748047, "t": 403.392578125, "r": 535.3616943359375, "b": 382.1797790527344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 179]}], "orig": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i.", "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 353.69927978515625, "r": 323.3839111328125, "b": 338.936279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "1.2 Current state of IBM i security", "text": "1.2 Current state of IBM i security", "level": 1}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 321.37127685546875, "r": 547.3182373046875, "b": 276.1588439941406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 306]}], "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE.", "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 263.3522644042969, "r": 547.284423828125, "b": 206.1400604248047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 405]}], "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data.", "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 193.33349609375, "r": 547.2832641601562, "b": 112.12167358398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 640]}], "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data.", "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 72.8219985961914, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 87.84030151367188, "t": 36.461997985839844, "r": 328.7253723144531, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 72.8219985961914, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 87.84030151367188, "t": 36.461997985839844, "r": 328.7253723144531, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 544.3033447265625, "b": 639.2794189453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 589]}], "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage.", "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"self_ref": "#/texts/74", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 64.80000305175781, "t": 618.665283203125, "r": 301.4690246582031, "b": 606.67724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "1.3.1 Existing row and column control", "text": "1.3.1 Existing row and column control", "level": 1}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 592.5112915039062, "r": 541.5673828125, "b": 535.2990112304688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 377]}], "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator.", "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 522.492431640625, "r": 547.4407958984375, "b": 477.27996826171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 340]}], "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases.", "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 464.473388671875, "r": 547.232666015625, "b": 431.2607727050781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 247]}], "orig": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view.", "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 136.8000030517578, "t": 100.18199920654297, "r": 316.447265625, "b": 91.85700225830078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "Figure 1-2 Existing row and column controls", "text": "Figure 1-2 Existing row and column controls"}, {"self_ref": "#/texts/79", "parent": {"cref": "#/pictures/8"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 180.95911, "t": 408.54388, "r": 209.08017, "b": 402.9216, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "User with", "text": "User with"}, {"self_ref": "#/texts/80", "parent": {"cref": "#/pictures/8"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 170.00624, "t": 401.04749, "r": 220.10355, "b": 395.42519999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "*ALLOBJ access", "text": "*ALLOBJ access"}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 720.665283203125, "r": 335.4955139160156, "b": 708.67724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "2.1.6 Change Function Usage CL command", "text": "2.1.6 Change Function Usage CL command", "level": 1}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 694.5112915039062, "r": 547.284423828125, "b": 685.2982788085938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 677.4717407226562, "r": 301.5174865722656, "b": 668.2587280273438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )", "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/84", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.80099487304688, "t": 665.471923828125, "r": 313.39776611328125, "b": 656.2589111328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )", "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/85", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.8009796142578, "t": 653.4721069335938, "r": 310.8171081542969, "b": 644.2590942382812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )", "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.7999725341797, "t": 631.5123291015625, "r": 512.5380249023438, "b": 610.2994995117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 126]}], "orig": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:", "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"self_ref": "#/texts/87", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.80096435546875, "t": 602.3235473632812, "r": 441.59686279296875, "b": 593.5487670898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 572.6453247070312, "r": 544.4754638671875, "b": 560.6572875976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "level": 1}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 546.4913330078125, "r": 519.5179443359375, "b": 525.2785034179688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view.", "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"self_ref": "#/texts/90", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 512.4420166015625, "r": 283.9680480957031, "b": 504.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "Table 2-1 FUNCTION_USAGE view", "text": "Table 2-1 FUNCTION_USAGE view"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 339.49127197265625, "r": 547.2803955078125, "b": 318.2784729003906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 112]}], "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1.", "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"self_ref": "#/texts/92", "parent": {"cref": "#/body"}, "children": [], "label": "paragraph", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 305.4420166015625, "r": 462.35418701171875, "b": 297.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 74]}], "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"self_ref": "#/texts/93", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 288.34198, "r": 171.26956, "b": 279.56719999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "SELECT", "text": "SELECT"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 182.75941, "t": 288.34198, "r": 251.69853, "b": 279.56719999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "function_id,", "text": "function_id,"}, {"self_ref": "#/texts/95", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 166.78244, "t": 276.3421599999999, "r": 241.73852999999997, "b": 267.56737999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "user_name,", "text": "user_name,"}, {"self_ref": "#/texts/96", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 170.75961, "t": 264.34235, "r": 221.69901999999996, "b": 255.56758000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "usage,", "text": "usage,"}, {"self_ref": "#/texts/97", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 167.53809, "t": 252.34253, "r": 236.69878, "b": 243.56777999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "user_type", "text": "user_type"}, {"self_ref": "#/texts/98", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 240.34272999999996, "r": 160.59396, "b": 231.56798000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "FROM", "text": "FROM"}, {"self_ref": "#/texts/99", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 178.43944, "t": 240.34272999999996, "r": 261.71829, "b": 231.56798000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "function_usage", "text": "function_usage"}, {"self_ref": "#/texts/100", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 228.34293000000002, "r": 162.44176, "b": 219.56817999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "WHERE", "text": "WHERE"}, {"self_ref": "#/texts/101", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 177.8268, "t": 228.34293000000002, "r": 331.67731, "b": 219.56817999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "function_id=\u2019QIBM_DB_SECADM\u2019", "text": "function_id=\u2019QIBM_DB_SECADM\u2019"}, {"self_ref": "#/texts/102", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 216.34312, "r": 178.77542, "b": 207.56836999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "ORDER BY", "text": "ORDER BY"}, {"self_ref": "#/texts/103", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 189.26929, "t": 216.34312, "r": 241.73856, "b": 207.56836999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "user_name;", "text": "user_name;"}, {"self_ref": "#/texts/104", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 171.7793731689453, "r": 249.59605407714844, "b": 157.01637268066406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "2.2 Separation of duties", "text": "2.2 Separation of duties", "level": 1}, {"self_ref": "#/texts/105", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 139.45127868652344, "r": 547.2234497070312, "b": 82.23904418945312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 463]}], "orig": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"self_ref": "#/texts/106", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/107", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/108", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 542.6943359375, "b": 651.2788696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 516]}], "orig": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security.", "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security."}, {"self_ref": "#/texts/109", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 638.4722900390625, "r": 547.303955078125, "b": 593.2598266601562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 285]}], "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table.", "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"self_ref": "#/texts/110", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 580.5130615234375, "r": 538.6507568359375, "b": 559.3002319335938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 129]}], "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group.", "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"self_ref": "#/texts/111", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 546.49365234375, "r": 545.7960205078125, "b": 513.281005859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 204]}], "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table.", "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"self_ref": "#/texts/112", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 500.47442626953125, "r": 539.80712890625, "b": 455.2619934082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 285]}], "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself.", "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"self_ref": "#/texts/113", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 442.5151672363281, "r": 543.067138671875, "b": 421.3023681640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 136]}], "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools.", "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"self_ref": "#/texts/114", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 64.80000305175781, "t": 408.4620056152344, "r": 391.754638671875, "b": 400.1369934082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 78]}], "orig": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority"}, {"self_ref": "#/texts/115", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 355.32000732421875, "t": 36.461997985839844, "r": 523.5407104492188, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Chapter 2. Roles and separation of duties", "text": "Chapter 2. Roles and separation of duties"}, {"self_ref": "#/texts/116", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/117", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 136.799560546875, "t": 720.490966796875, "r": 528.7305908203125, "b": 699.2781372070312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 135]}], "orig": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.", "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules."}, {"self_ref": "#/texts/118", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 377.86199951171875, "r": 341.9765930175781, "b": 369.5369873046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 42]}], "orig": "Figure 3-1 CREATE PERMISSION SQL statement", "text": "Figure 3-1 CREATE PERMISSION SQL statement"}, {"self_ref": "#/texts/119", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 346.56491, "t": 670.53748, "r": 530.74371, "b": 662.66492, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "Names the row permission for row access control", "text": "Names the row permission for row access control"}, {"self_ref": "#/texts/120", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 652.32031, "r": 246.7961, "b": 642.49017, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CREATE PERMISSION", "text": "CREATE PERMISSION"}, {"self_ref": 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625.70587, "r": 450.77191000000005, "b": 617.83331, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Identifies the table on which the row", "text": "Identifies the table on which the row"}, {"self_ref": "#/texts/125", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 450.86123999999995, "t": 625.70587, "r": 529.93134, "b": 617.83331, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "permission is created", "text": "permission is created"}, {"self_ref": "#/texts/126", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 610.93744, "r": 163.45079, "b": 601.1073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "ON", "text": "ON"}, {"self_ref": "#/texts/127", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 168.58405, "t": 610.93744, "r": 174.30298, "b": 601.11877, 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{"self_ref": "#/texts/167", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 352.0559997558594, "r": 215.37600708007812, "b": 340.95599365234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Column mask", "text": "Column mask", "level": 1}, {"self_ref": "#/texts/168", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 336.9112854003906, "r": 542.7664794921875, "b": 291.6988525390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 297]}], "orig": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number.", "text": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number."}, {"self_ref": "#/texts/169", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/170", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15"}, {"self_ref": "#/texts/171", "parent": {"cref": "#/body"}, "children": [], "label": "paragraph", "prov": [{"page_no": 11, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 412.20758056640625, "b": 711.2779541015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "Table 3-1 summarizes these special registers and their values.", "text": "Table 3-1 summarizes these special registers and their values."}, {"self_ref": "#/texts/172", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 698.501953125, "r": 372.6036376953125, "b": 690.177001953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "Table 3-1 Special registers and their corresponding values", "text": "Table 3-1 Special registers and their corresponding values"}, {"self_ref": "#/texts/173", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 577.5112915039062, "r": 538.493896484375, "b": 556.2984619140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:", "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"self_ref": "#/texts/174", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 548.471923828125, "r": 411.36138916015625, "b": 539.2589111328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "GLYPH<SM590000> A user connects to the server using the user profile ALICE.", "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/175", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 531.4921264648438, "r": 453.2580871582031, "b": 522.2791137695312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE.", "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/176", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 514.5123291015625, "r": 541.4498291015625, "b": 493.29949951171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 160]}], "orig": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/177", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 485.472900390625, "r": 547.2167358398438, "b": 452.2602844238281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 253]}], "orig": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/178", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.80101013183594, "t": 444.49346923828125, "r": 547.3540649414062, "b": 423.2806701660156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 133]}], "orig": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/179", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 195.2821044921875, "r": 341.2566223144531, "b": 186.95709228515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "Figure 3-5 Special registers and adopted authority", "text": "Figure 3-5 Special registers and adopted authority"}, {"self_ref": "#/texts/180", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 140.7323, "t": 405.01547, "r": 218.71170000000004, "b": 396.50473, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "SignedonasALICE Signed on as ALICE", "text": "SignedonasALICE Signed on as ALICE"}, {"self_ref": "#/texts/181", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 381.12558000000007, "r": 191.70256, "b": 372.61484, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/182", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 369.18066, "r": 232.56117, "b": 360.66992, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "CURRENT USER = ALICE", "text": "CURRENT USER = ALICE"}, {"self_ref": "#/texts/183", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 345.29076999999995, "r": 183.26944, "b": 336.78003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "CALL proc1", "text": "CALL proc1"}, {"self_ref": "#/texts/184", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 318.41476, "r": 184.17328, "b": 309.90402, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "P1 Proc1:", "text": "P1 Proc1:"}, {"self_ref": "#/texts/185", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 157.52185, "t": 306.46985, "r": 209.103, "b": 297.95911, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Owner = JOE", "text": "Owner = JOE"}, {"self_ref": "#/texts/186", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 157.52185, "t": 294.52493, "r": 281.68927, "b": 286.01419, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "SET OPTION USRPRF=*OWNER", "text": "SET OPTION USRPRF=*OWNER"}, {"self_ref": "#/texts/187", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 270.63507000000004, "r": 201.65666, "b": 262.12433, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/188", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 258.69016, "r": 234.57686999999999, "b": 250.17940999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "CURRENT USER = JOE", "text": "CURRENT USER = JOE"}, {"self_ref": "#/texts/189", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 225.84158000000002, "r": 191.70256, "b": 217.33083, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/190", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 213.89666999999997, "r": 232.56117, "b": 205.38590999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "CURRENT USER = ALICE", "text": "CURRENT USER = ALICE"}, {"self_ref": "#/texts/191", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 64.80000305175781, "t": 166.44528198242188, "r": 247.02536010742188, "b": 154.457275390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "3.2.2 Built-in global variables", "text": "3.2.2 Built-in global variables", "level": 1}, {"self_ref": "#/texts/192", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 140.29127502441406, "r": 518.0011596679688, "b": 119.0784683227539, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 161]}], "orig": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables.", "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"self_ref": "#/texts/193", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 106.27189636230469, "r": 532.3385009765625, "b": 73.05928039550781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 233]}], "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic.", "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"self_ref": "#/texts/194", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/195", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "19", "text": "19"}, {"self_ref": "#/texts/196", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "20", "text": "20"}, {"self_ref": "#/texts/197", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/198", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 342.5477294921875, "b": 711.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "Table 3-2 lists the nine built-in global variables.", "text": "Table 3-2 lists the nine built-in global variables."}, {"self_ref": "#/texts/199", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 698.501953125, "r": 201.1814727783203, "b": 690.177001953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Table 3-2 Built-in global variables", "text": "Table 3-2 Built-in global variables"}, {"self_ref": "#/texts/200", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 469.7992858886719, "r": 384.3638916015625, "b": 455.0362854003906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text": "3.3 VERIFY_GROUP_FOR_USER function", "level": 1}, {"self_ref": "#/texts/201", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8000030517578, "t": 437.4712829589844, "r": 547.2347412109375, "b": 356.2593994140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 576]}], "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error.", "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"self_ref": "#/texts/202", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 343.5125732421875, "r": 547.2573852539062, "b": 310.2999572753906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 235]}], "orig": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value.", "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"self_ref": "#/texts/203", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 297.4933776855469, "r": 458.44525146484375, "b": 288.2803955078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"self_ref": "#/texts/204", "parent": {"cref": "#/groups/8"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 280.45379638671875, "r": 406.0775146484375, "b": 271.2408142089844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "1. There are user profiles for MGR, JANE, JUDY, and TONY.", "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/205", "parent": {"cref": "#/groups/8"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 263.4739990234375, "r": 396.9881591796875, "b": 254.26100158691406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "2. The user profile JANE specifies a group profile of MGR.", "text": "2. The user profile JANE specifies a group profile of MGR.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/206", "parent": {"cref": "#/groups/8"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 246.4941864013672, "r": 536.568603515625, "b": 225.28138732910156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 127]}], "orig": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/207", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 12, "bbox": {"l": 151.20018005371094, "t": 217.305419921875, "r": 451.01605224609375, "b": 150.57144165039062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 265]}], "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"self_ref": "#/texts/208", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 720.341552734375, "r": 166.73934936523438, "b": 711.5667724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "RETURN", "text": "RETURN"}, {"self_ref": "#/texts/209", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 708.3417358398438, "r": 156.7793426513672, "b": 699.5669555664062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "CASE", "text": "CASE"}, {"self_ref": "#/texts/210", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 696.3419189453125, "r": 521.5742797851562, "b": 531.5695190429688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 437]}], "orig": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;", "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"self_ref": "#/texts/211", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 516.4940795898438, "r": 547.2122192382812, "b": 495.2812805175781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 136]}], "orig": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/212", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 487.51446533203125, "r": 469.1528015136719, "b": 478.3014831542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "-Human Resources can see the unmasked TAX_ID of the employees.", "text": "-Human Resources can see the unmasked TAX_ID of the employees.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/213", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 470.4748840332031, "r": 403.95953369140625, "b": 461.26190185546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "-Employees can see only their own unmasked TAX_ID.", "text": "-Employees can see only their own unmasked TAX_ID.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/214", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 453.4950866699219, "r": 545.16845703125, "b": 432.28228759765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 129]}], "orig": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/215", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 424.5154724121094, "r": 529.463623046875, "b": 415.302490234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/216", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 151.1997833251953, "t": 407.47589111328125, "r": 530.060302734375, "b": 398.2629089355469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/217", "parent": {"cref": "#/body"}, "children": [], "label": "paragraph", "prov": [{"page_no": 13, "bbox": {"l": 136.8000030517578, "t": 385.48199462890625, "r": 351.9873046875, "b": 377.156982421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"self_ref": "#/texts/218", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 13, "bbox": {"l": 136.8000030517578, "t": 368.3218994140625, "r": 526.5546875, "b": 107.55116271972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 590]}], "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;", "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}, {"self_ref": "#/texts/219", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/220", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "27", "text": "27"}, {"self_ref": "#/texts/221", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 449.952392578125, "b": 711.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/222", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 618.4619750976562, "r": 293.1380920410156, "b": 610.1370239257812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Figure 3-10 Column masks shown in System i Navigator", "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"self_ref": "#/texts/223", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 589.6253051757812, "r": 203.98521423339844, "b": 577.6372680664062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "3.6.6 Activating RCAC", "text": "3.6.6 Activating RCAC", "level": 1}, {"self_ref": "#/texts/224", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 563.4713134765625, "r": 547.2256469726562, "b": 530.2586669921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 265]}], "orig": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/groups/11"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 522.4918823242188, "r": 409.4788818359375, "b": 513.2788696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "1. Run the SQL statements that are shown in Example 3-10.", "text": "1. Run the SQL statements that are shown in Example 3-10.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/226", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 500.4420166015625, "r": 375.2909851074219, "b": 492.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table", "text": "Example 3-10 Activating RCAC on the EMPLOYEES table", "level": 1}, {"self_ref": "#/texts/227", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 483.3418884277344, "r": 376.6766052246094, "b": 474.5671081542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "/* Active Row Access Control (permissions) */", "text": "/* Active Row Access Control (permissions) */", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/228", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 471.3420715332031, "r": 354.86962890625, "b": 462.5672912597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "/* Active Column Access Control (masks)", "text": "/* Active Column Access Control (masks)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/229", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 365.77313232421875, "t": 471.3420715332031, "r": 376.6766052246094, "b": 462.5672912597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "*/", "text": "*/"}, {"self_ref": "#/texts/230", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 459.3422546386719, "r": 291.7178039550781, "b": 450.5674743652344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"self_ref": "#/texts/231", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 447.3424377441406, "r": 271.6783142089844, "b": 438.5676574707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "ACTIVATE ROW ACCESS CONTROL", "text": "ACTIVATE ROW ACCESS CONTROL"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 435.3426208496094, "r": 291.7178039550781, "b": 426.5678405761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"self_ref": "#/texts/233", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 411.4924011230469, "r": 540.8014526367188, "b": 378.27978515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/234", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 142.9621124267578, "r": 347.4305419921875, "b": 134.63710021972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "28", "text": "28"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 15, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 514.048583984375, "b": 687.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 228]}], "orig": "2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause.", "text": "2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 136.8000030517578, "t": 311.4420166015625, "r": 327.0932922363281, "b": 303.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 44]}], "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text": "Figure 4-68 Visual Explain with RCAC enabled"}, {"self_ref": "#/texts/239", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 15, "bbox": {"l": 136.8000030517578, "t": 285.4313659667969, "r": 547.2394409179688, "b": 252.21875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 232]}], "orig": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause.", "text": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 64.80000305175781, "t": 124.48210144042969, "r": 227.1014862060547, "b": 116.15709686279297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Figure 4-69 Index advice with no RCAC", "text": "Figure 4-69 Index advice with no RCAC"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 214.8000030517578, "t": 36.461997985839844, "r": 523.5935668945312, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example", "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}, {"self_ref": "#/texts/242", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "77", "text": "77"}, {"self_ref": "#/texts/243", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 16, "bbox": {"l": 64.80030822753906, "t": 720.3270263671875, "r": 500.697265625, "b": 85.39237976074219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1998]}], "orig": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;", "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}, {"self_ref": "#/texts/244", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 83.98200225830078, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "124", "text": "124"}, {"self_ref": "#/texts/245", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 98.94000244140625, "t": 36.461997985839844, "r": 339.819580078125, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 287.2200012207031, "t": 763.4519653320312, "r": 414.24481201171875, "b": 741.251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Back cover", "text": "Back cover"}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 18, "bbox": {"l": 27.0, "t": 718.3619995117188, "r": 447.3600158691406, "b": 651.5399780273438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i", "level": 1}, {"self_ref": "#/texts/248", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 549.8280029296875, "r": 127.443603515625, "b": 525.1680297851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Implement roles and separation of duties", "text": "Implement roles and separation of duties"}, {"self_ref": "#/texts/249", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 507.8280334472656, "r": 120.283203125, "b": 469.1280212402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Leverage row permissions on the database", "text": "Leverage row permissions on the database"}, {"self_ref": "#/texts/250", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 451.8480224609375, "r": 121.44960021972656, "b": 413.14801025390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Protect columns by defining column masks", "text": "Protect columns by defining column masks"}, {"self_ref": "#/texts/251", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 152.94000244140625, "t": 549.2714233398438, "r": 414.084228515625, "b": 468.4081115722656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 464]}], "orig": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "text": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"self_ref": "#/texts/252", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 152.9400177001953, "t": 460.292724609375, "r": 414.173828125, "b": 403.4290466308594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"self_ref": "#/texts/253", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 18, "bbox": {"l": 171.0, "t": 160.66200256347656, "r": 231.8876953125, "b": 152.3369903564453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "REDP-5110-00", "text": "REDP-5110-00"}, {"self_ref": "#/texts/254", "parent": {"cref": "#/pictures/15"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 558.11987, "t": 746.5313100000001, "r": 565.46039, "b": 737.3183, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "fi", "text": "fi"}, {"self_ref": "#/texts/255", "parent": {"cref": "#/pictures/16"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 474.60001, "t": 627.94342, "r": 580.88989, "b": 603.05902, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Redpaper", "text": "Redpaper"}, {"self_ref": "#/texts/256", "parent": {"cref": "#/pictures/16"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 582.53992, "t": 619.67285, "r": 592.13989, "b": 610.79285, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "\u2122", "text": "\u2122"}, {"self_ref": "#/texts/257", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 544.2816772460938, "r": 559.809326171875, "b": 489.8393859863281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 44]}], "orig": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION", "text": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION"}, {"self_ref": "#/texts/258", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 440.2080078125, "r": 587.38916015625, "b": 405.52801513671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE", "text": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE"}, {"self_ref": "#/texts/259", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 392.13970947265625, "r": 587.5205078125, "b": 250.36593627929688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 323]}], "orig": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment.", "text": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. 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"end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449462890625, "t": 551.89306640625, "r": 547.1211547851562, "b": 542.6800537109375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 529.3934326171875, "r": 536.0958862304688, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6468505859375, "t": 529.3934326171875, "r": 547.1978149414062, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808044433594, "t": 517.3936157226562, "r": 549.8472290039062, "b": 508.18060302734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 504.85394287109375, "r": 536.1293334960938, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6611328125, "t": 504.85394287109375, "r": 547.19287109375, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 492.3740539550781, "r": 549.8472290039062, "b": 483.16107177734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 479.8941650390625, "r": 536.0551147460938, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015014648438, "t": 479.8941650390625, "r": 547.14794921875, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 467.3545227050781, "r": 536.080078125, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.635498046875, "t": 467.3545227050781, "r": 547.19091796875, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.7970428466797, "t": 444.8548889160156, "r": 536.0908813476562, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.642822265625, "t": 444.8548889160156, "r": 547.1947631835938, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.7970428466797, "t": 432.8550720214844, "r": 536.1271362304688, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6658935546875, "t": 432.8550720214844, "r": 547.2047119140625, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 420.37518310546875, "r": 535.9526977539062, "b": 411.1622009277344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.5558471679688, "t": 420.37518310546875, "r": 547.1590576171875, "b": 411.1622009277344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, 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{"bbox": {"l": 151.19720458984375, "t": 395.35565185546875, "r": 536.0748901367188, "b": 386.1426696777344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6302490234375, "t": 395.35565185546875, "r": 547.1856079101562, "b": 386.1426696777344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 382.8757629394531, "r": 411.2704772949219, "b": 373.66278076171875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.8177490234375, "t": 382.8757629394531, "r": 547.1786499023438, "b": 373.66278076171875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 370.3958740234375, "r": 536.035888671875, "b": 361.1828918457031, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", 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"coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 345.3763427734375, "r": 530.5352172851562, "b": 336.1633605957031, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755004882812, "t": 345.3763427734375, "r": 547.156005859375, "b": 336.1633605957031, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.7970428466797, "t": 332.8964538574219, "r": 547.256591796875, "b": 323.6834716796875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79702758789062, "t": 310.3968200683594, "r": 530.5396118164062, "b": 301.183837890625, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0916748046875, "t": 310.3968200683594, "r": 547.19580078125, "b": 301.183837890625, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79702758789062, "t": 298.3970031738281, "r": 530.4808959960938, "b": 289.18402099609375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 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"end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1971893310547, "t": 273.3774719238281, "r": 530.4347534179688, "b": 264.16448974609375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.9962158203125, "t": 273.3774719238281, "r": 547.1190795898438, "b": 264.16448974609375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79901123046875, "t": 646.8715209960938, "r": 189.86537170410156, "b": 637.6585083007812, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Trademarks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 195.3968505859375, "t": 646.8715209960938, "r": 547.182861328125, "b": 637.6585083007812, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, 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"Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79803466796875, "t": 576.852783203125, "r": 339.18292236328125, "b": 567.6397705078125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.714111328125, "t": 576.852783203125, "r": 547.1387939453125, "b": 567.6397705078125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, 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Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6468505859375, "t": 529.3934326171875, "r": 547.1978149414062, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79808044433594, "t": 517.3936157226562, "r": 549.8472290039062, "b": 508.18060302734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79806518554688, "t": 504.85394287109375, "r": 536.1293334960938, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6611328125, "t": 504.85394287109375, "r": 547.19287109375, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79806518554688, "t": 492.3740539550781, "r": 549.8472290039062, "b": 483.16107177734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 151.19720458984375, "t": 479.8941650390625, "r": 536.0551147460938, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015014648438, "t": 479.8941650390625, "r": 547.14794921875, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 151.19720458984375, "t": 467.3545227050781, "r": 536.080078125, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.635498046875, "t": 467.3545227050781, "r": 547.19091796875, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.7970428466797, "t": 444.8548889160156, "r": 536.0908813476562, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.642822265625, "t": 444.8548889160156, "r": 547.1947631835938, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.7970428466797, "t": 432.8550720214844, "r": 536.1271362304688, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, 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{"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 464.6240539550781, "r": 488.1546630859375, "b": 447.0404968261719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 122]}], "orig": "No one else has the vast consulting experiences, skills sharing and renown service offerings to do what we can do for you.", "text": "No one else has the vast consulting experiences, skills sharing and renown service offerings to do what we can do for you."}, {"self_ref": "#/texts/23", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 434.6739807128906, "r": 367.8602294921875, "b": 427.2699890136719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "Because no one else is IBM.", "text": "Because no one else is IBM."}, {"self_ref": "#/texts/24", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 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The DB2 for i Center of Excellence (CoE) can help you achieve-perhaps reexamine and exceed-your business requirements and gain more confidence and satisfaction in IBM product data management products and solutions.", "text": "With combined experiences and direct access to development groups, we're the experts in IBM DB2\u00ae for i. The DB2 for i Center of Excellence (CoE) can help you achieve-perhaps reexamine and exceed-your business requirements and gain more confidence and satisfaction in IBM product data management products and solutions."}, {"self_ref": "#/texts/25", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 354.1459655761719, "r": 434.8320617675781, "b": 345.1319274902344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Who we are, some of what we do", "text": "Who we are, some of what we do", "level": 1}, {"self_ref": "#/texts/26", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 342.6517639160156, "r": 434.56317138671875, "b": 335.2477722167969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "Global CoE engagements cover topics including:", "text": "Global CoE engagements cover topics including:"}, {"self_ref": "#/texts/27", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 322.8817443847656, "r": 401.5641174316406, "b": 315.4777526855469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "r Database performance and scalability", "text": "r Database performance and scalability", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/28", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 312.69903564453125, "r": 424.9964599609375, "b": 305.2950439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 44]}], "orig": "r Advanced SQL knowledge and skills transfer", "text": "r Advanced SQL knowledge and skills transfer", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/29", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 302.5164489746094, "r": 392.158447265625, "b": 295.1124572753906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "r Business intelligence and analytics", "text": "r Business intelligence and analytics", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/30", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 292.333740234375, "r": 339.94354248046875, "b": 284.92974853515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "r DB2 Web Query", "text": "r DB2 Web Query", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/31", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 282.1511535644531, "r": 504.1931457519531, "b": 274.7471618652344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "r Query/400 modernization for better reporting and analysis capabilities", "text": "r Query/400 modernization for better reporting and analysis capabilities", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/32", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 271.96844482421875, "r": 423.002197265625, "b": 264.564453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "r Database modernization and re-engineering", "text": "r Database modernization and re-engineering", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/33", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 261.7858581542969, "r": 399.6517333984375, "b": 254.38186645507812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "r Data-centric architecture and design", "text": "r Data-centric architecture and design", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/34", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 251.60325622558594, "r": 466.77880859375, "b": 244.1992645263672, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "r Extremely large database and overcoming limits to growth", "text": "r Extremely large database and overcoming limits to growth", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/35", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 241.42054748535156, "r": 382.2095642089844, "b": 234.0165557861328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "r ISV education and enablement", "text": "r ISV education and enablement", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/36", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 64.80000305175781, "t": 718.1519775390625, "r": 151.46160888671875, "b": 695.9519653320312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Preface", "text": "Preface", "level": 1}, {"self_ref": "#/texts/37", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.79983520507812, "t": 659.3513793945312, "r": 547.3082275390625, "b": 590.1392822265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 469]}], "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"self_ref": "#/texts/38", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.79986572265625, "t": 577.3925170898438, "r": 546.4656982421875, "b": 532.1800537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"self_ref": "#/texts/39", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.8000030517578, "t": 471.37127685546875, "r": 547.2366943359375, "b": 450.1584777832031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 172]}], "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"self_ref": "#/texts/40", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 263.3995666503906, "t": 416.3512268066406, "r": 541.2507934570312, "b": 275.1402587890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 684]}], "orig": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office.", "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"self_ref": "#/texts/41", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 64.80000305175781, "t": 36.461997985839844, "r": 257.24334716796875, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"self_ref": "#/texts/42", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 263.39959716796875, "t": 264.37347412109375, "r": 541.2737426757812, "b": 111.162841796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 726]}], "orig": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com .", "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"self_ref": "#/texts/43", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 538.8599853515625, "t": 37.15127944946289, "r": 547.2503051757812, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "xi", "text": "xi"}, {"self_ref": "#/texts/44", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 64.80000305175781, "t": 503.69940185546875, "r": 125.36660766601562, "b": 488.9364013671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Authors", "text": "Authors", "level": 1}, {"self_ref": "#/texts/45", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 81.0, "t": 523.457275390625, "r": 115.13253021240234, "b": 517.019287109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Chapter 1.", "text": "Chapter 1."}, {"self_ref": "#/texts/46", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 500.3999938964844, "t": 698.831298828125, "r": 522.6177368164062, "b": 661.8682861328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/47", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 537.1136474609375, "r": 547.3047485351562, "b": 482.1217956542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "Securing and protecting IBM DB2 data", "text": "Securing and protecting IBM DB2 data", "level": 1}, {"self_ref": "#/texts/48", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.79965209960938, "t": 443.2912902832031, "r": 547.2540283203125, "b": 362.078857421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 648]}], "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record.", "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"self_ref": "#/texts/49", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.80023193359375, "t": 349.27227783203125, "r": 527.206298828125, "b": 304.0598449707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 304]}], "orig": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement.", "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"self_ref": "#/texts/50", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 291.3130187988281, "r": 547.1551513671875, "b": 270.1002197265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 122]}], "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:", "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:"}, {"self_ref": "#/texts/51", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 262.2736511230469, "r": 250.23167419433594, "b": 253.06063842773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "GLYPH<SM590000> Security fundamentals", "text": "GLYPH<SM590000> Security fundamentals", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/52", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 250.27383422851562, "r": 282.98114013671875, "b": 241.0608367919922, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "GLYPH<SM590000> Current state of IBM i security", "text": "GLYPH<SM590000> Current state of IBM i security", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/53", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 238.27403259277344, "r": 264.8818664550781, "b": 229.06103515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "GLYPH<SM590000> DB2 for i security controls", "text": "GLYPH<SM590000> DB2 for i security controls", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/54", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 74.24993896484375, "r": 258.362548828125, "b": 67.21955871582031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "$^{1 }$http://www.idtheftcenter.org", "text": "$^{1 }$http://www.idtheftcenter.org"}, {"self_ref": "#/texts/55", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 64.40973663330078, "r": 234.05880737304688, "b": 57.02824020385742, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "$^{2 }$http://www.ponemon.org /", "text": "$^{2 }$http://www.ponemon.org /"}, {"self_ref": "#/texts/56", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 64.80000305175781, "t": 36.461997985839844, "r": 257.24334716796875, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"self_ref": "#/texts/57", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 541.6798706054688, "t": 37.15127944946289, "r": 547.2176513671875, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 717.6593017578125, "r": 267.40582275390625, "b": 702.8963012695312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "1.1 Security fundamentals", "text": "1.1 Security fundamentals", "level": 1}, {"self_ref": "#/texts/59", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 685.3912963867188, "r": 545.0048217773438, "b": 664.178466796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 133]}], "orig": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:", "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"self_ref": "#/texts/60", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 656.8751220703125, "r": 547.1642456054688, "b": 611.138916015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 361]}], "orig": "GLYPH<SM590000> First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "text": "GLYPH<SM590000> First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/61", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 151.199462890625, "t": 603.3721313476562, "r": 547.2608642578125, "b": 522.1602172851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 587]}], "orig": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 151.199462890625, "t": 514.3934326171875, "r": 541.9920043945312, "b": 505.180419921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "A security policy is what defines whether the system and its settings are secure (or not).", "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"self_ref": "#/texts/63", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 136.79930114746094, "t": 497.8750305175781, "r": 547.1582641601562, "b": 416.139404296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8002166748047, "t": 403.392578125, "r": 535.3616943359375, "b": 382.1797790527344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 179]}], "orig": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i.", "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 353.69927978515625, "r": 323.3839111328125, "b": 338.936279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "1.2 Current state of IBM i security", "text": "1.2 Current state of IBM i security", "level": 1}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 321.37127685546875, "r": 547.3182373046875, "b": 276.1588439941406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 306]}], "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE.", "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 263.3522644042969, "r": 547.284423828125, "b": 206.1400604248047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 405]}], "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data.", "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 193.33349609375, "r": 547.2832641601562, "b": 112.12167358398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 640]}], "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data.", "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 72.8219985961914, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 87.84030151367188, "t": 36.461997985839844, "r": 328.7253723144531, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 72.8219985961914, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 87.84030151367188, "t": 36.461997985839844, "r": 328.7253723144531, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 544.3033447265625, "b": 639.2794189453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 589]}], "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage.", "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"self_ref": "#/texts/74", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 64.80000305175781, "t": 618.665283203125, "r": 301.4690246582031, "b": 606.67724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "1.3.1 Existing row and column control", "text": "1.3.1 Existing row and column control", "level": 1}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 592.5112915039062, "r": 541.5673828125, "b": 535.2990112304688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 377]}], "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator.", "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 522.492431640625, "r": 547.4407958984375, "b": 477.27996826171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 340]}], "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases.", "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 464.473388671875, "r": 547.232666015625, "b": 431.2607727050781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 247]}], "orig": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view.", "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 136.8000030517578, "t": 100.18199920654297, "r": 316.447265625, "b": 91.85700225830078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "Figure 1-2 Existing row and column controls", "text": "Figure 1-2 Existing row and column controls"}, {"self_ref": "#/texts/79", "parent": {"cref": "#/pictures/8"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 180.95911, "t": 408.54388, "r": 209.08017, "b": 402.9216, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "User with", "text": "User with"}, {"self_ref": "#/texts/80", "parent": {"cref": "#/pictures/8"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 170.00624, "t": 401.04749, "r": 220.10355, "b": 395.42519999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "*ALLOBJ access", "text": "*ALLOBJ access"}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 720.665283203125, "r": 335.4955139160156, "b": 708.67724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "2.1.6 Change Function Usage CL command", "text": "2.1.6 Change Function Usage CL command", "level": 1}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 694.5112915039062, "r": 547.284423828125, "b": 685.2982788085938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 677.4717407226562, "r": 301.5174865722656, "b": 668.2587280273438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )", "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/84", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.80099487304688, "t": 665.471923828125, "r": 313.39776611328125, "b": 656.2589111328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )", "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/85", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.8009796142578, "t": 653.4721069335938, "r": 310.8171081542969, "b": 644.2590942382812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )", "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.7999725341797, "t": 631.5123291015625, "r": 512.5380249023438, "b": 610.2994995117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 126]}], "orig": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:", "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"self_ref": "#/texts/87", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.80096435546875, "t": 602.3235473632812, "r": 441.59686279296875, "b": 593.5487670898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 572.6453247070312, "r": 544.4754638671875, "b": 560.6572875976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "level": 1}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 546.4913330078125, "r": 519.5179443359375, "b": 525.2785034179688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view.", "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"self_ref": "#/texts/90", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 512.4420166015625, "r": 283.9680480957031, "b": 504.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "Table 2-1 FUNCTION_USAGE view", "text": "Table 2-1 FUNCTION_USAGE view"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 339.49127197265625, "r": 547.2803955078125, "b": 318.2784729003906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 112]}], "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1.", "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"self_ref": "#/texts/92", "parent": {"cref": "#/body"}, "children": [], "label": "paragraph", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 305.4420166015625, "r": 462.35418701171875, "b": 297.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 74]}], "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"self_ref": "#/texts/93", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 288.34198, "r": 171.26956, "b": 279.56719999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "SELECT", "text": "SELECT"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 182.75941, "t": 288.34198, "r": 251.69853, "b": 279.56719999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "function_id,", "text": "function_id,"}, {"self_ref": "#/texts/95", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 166.78244, "t": 276.3421599999999, "r": 241.73852999999997, "b": 267.56737999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "user_name,", "text": "user_name,"}, {"self_ref": "#/texts/96", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 170.75961, "t": 264.34235, "r": 221.69901999999996, "b": 255.56758000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "usage,", "text": "usage,"}, {"self_ref": "#/texts/97", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 167.53809, "t": 252.34253, "r": 236.69878, "b": 243.56777999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "user_type", "text": "user_type"}, {"self_ref": "#/texts/98", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 240.34272999999996, "r": 160.59396, "b": 231.56798000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "FROM", "text": "FROM"}, {"self_ref": "#/texts/99", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 178.43944, "t": 240.34272999999996, "r": 261.71829, "b": 231.56798000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "function_usage", "text": "function_usage"}, {"self_ref": "#/texts/100", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 228.34293000000002, "r": 162.44176, "b": 219.56817999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "WHERE", "text": "WHERE"}, {"self_ref": "#/texts/101", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 177.8268, "t": 228.34293000000002, "r": 331.67731, "b": 219.56817999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "function_id=\u2019QIBM_DB_SECADM\u2019", "text": "function_id=\u2019QIBM_DB_SECADM\u2019"}, {"self_ref": "#/texts/102", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 216.34312, "r": 178.77542, "b": 207.56836999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "ORDER BY", "text": "ORDER BY"}, {"self_ref": "#/texts/103", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 189.26929, "t": 216.34312, "r": 241.73856, "b": 207.56836999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "user_name;", "text": "user_name;"}, {"self_ref": "#/texts/104", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 171.7793731689453, "r": 249.59605407714844, "b": 157.01637268066406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "2.2 Separation of duties", "text": "2.2 Separation of duties", "level": 1}, {"self_ref": "#/texts/105", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 139.45127868652344, "r": 547.2234497070312, "b": 82.23904418945312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 463]}], "orig": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"self_ref": "#/texts/106", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/107", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/108", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 542.6943359375, "b": 651.2788696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 516]}], "orig": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security.", "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security."}, {"self_ref": "#/texts/109", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 638.4722900390625, "r": 547.303955078125, "b": 593.2598266601562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 285]}], "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table.", "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"self_ref": "#/texts/110", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 580.5130615234375, "r": 538.6507568359375, "b": 559.3002319335938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 129]}], "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group.", "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"self_ref": "#/texts/111", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 546.49365234375, "r": 545.7960205078125, "b": 513.281005859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 204]}], "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table.", "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"self_ref": "#/texts/112", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 500.47442626953125, "r": 539.80712890625, "b": 455.2619934082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 285]}], "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself.", "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"self_ref": "#/texts/113", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 442.5151672363281, "r": 543.067138671875, "b": 421.3023681640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 136]}], "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools.", "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"self_ref": "#/texts/114", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 64.80000305175781, "t": 408.4620056152344, "r": 391.754638671875, "b": 400.1369934082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 78]}], "orig": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority"}, {"self_ref": "#/texts/115", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 355.32000732421875, "t": 36.461997985839844, "r": 523.5407104492188, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Chapter 2. Roles and separation of duties", "text": "Chapter 2. Roles and separation of duties"}, {"self_ref": "#/texts/116", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/117", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 136.799560546875, "t": 720.490966796875, "r": 528.7305908203125, "b": 699.2781372070312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 135]}], "orig": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.", "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules."}, {"self_ref": "#/texts/118", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 377.86199951171875, "r": 341.9765930175781, "b": 369.5369873046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 42]}], "orig": "Figure 3-1 CREATE PERMISSION SQL statement", "text": "Figure 3-1 CREATE PERMISSION SQL statement"}, {"self_ref": "#/texts/119", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 346.56491, "t": 670.53748, "r": 530.74371, "b": 662.66492, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "Names the row permission for row access control", "text": "Names the row permission for row access control"}, {"self_ref": "#/texts/120", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 652.32031, "r": 246.7961, "b": 642.49017, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CREATE PERMISSION", "text": "CREATE PERMISSION"}, {"self_ref": 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625.70587, "r": 450.77191000000005, "b": 617.83331, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Identifies the table on which the row", "text": "Identifies the table on which the row"}, {"self_ref": "#/texts/125", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 450.86123999999995, "t": 625.70587, "r": 529.93134, "b": 617.83331, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "permission is created", "text": "permission is created"}, {"self_ref": "#/texts/126", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 610.93744, "r": 163.45079, "b": 601.1073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "ON", "text": "ON"}, {"self_ref": "#/texts/127", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 168.58405, "t": 610.93744, "r": 174.30298, "b": 601.11877, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "<", "text": "<"}, {"self_ref": "#/texts/128", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 174.30872, "t": 610.93744, "r": 226.86777, "b": 601.1073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "table name", "text": "table name"}, {"self_ref": "#/texts/129", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 226.86548000000002, "t": 610.93744, "r": 232.58441, "b": 601.11877, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ">", "text": ">"}, {"self_ref": "#/texts/130", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 235.79649, "t": 587.77161, "r": 406.62051, "b": 579.89905, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "Specifies an optional correlation name that ca", "text": "Specifies an optional correlation name that ca"}, {"self_ref": "#/texts/131", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 480.53094, "t": 587.77161, "r": 532.89496, "b": 579.89905, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "be used within search-condition", "text": "be used within search-condition"}, {"self_ref": "#/texts/132", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 569.5545, "r": 163.10973, "b": 559.72437, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "AS", "text": "AS"}, {"self_ref": "#/texts/133", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 165.68669, "t": 569.5545, "r": 171.40562, "b": 559.73584, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "<", "text": "<"}, {"self_ref": "#/texts/134", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 171.41136, "t": 569.5545, "r": 251.20424000000003, "b": 559.72437, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "correlation name", "text": "correlation name"}, {"self_ref": "#/texts/135", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 251.21115, "t": 569.5545, "r": 256.93008, "b": 559.73584, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ">", "text": ">"}, {"self_ref": "#/texts/136", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 321.56271, "t": 545.90588, "r": 455.3432, "b": 538.03333, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Indicates that a row permission is cr", "text": "Indicates that a row permission is cr"}, {"self_ref": "#/texts/137", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 455.20786000000004, "t": 545.90588, "r": 476.48404, "b": 538.03333, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "eated", "text": "eated"}, {"self_ref": "#/texts/138", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 528.17163, "r": 199.72467, "b": 518.34149, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "FOR ROWS", "text": "FOR ROWS"}, {"self_ref": "#/texts/139", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 321.5972, "t": 525.69733, "r": 444.0292400000001, "b": 517.82477, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Specifies a condition that can be", "text": "Specifies a condition that can be"}, {"self_ref": "#/texts/140", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 444.07986, "t": 525.69733, "r": 459.08678999999995, "b": 517.82477, 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all ref"}, {"self_ref": "#/texts/147", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 457.19281, "t": 477.41724, "r": 531.74939, "b": 469.54465, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "erences of the table", "text": "erences of the table"}, {"self_ref": "#/texts/148", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 459.20001, "r": 278.77805, "b": 449.36987, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "ENFORCED FOR ALL ACCESS", "text": "ENFORCED FOR ALL ACCESS"}, {"self_ref": "#/texts/149", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 312.28601, "t": 436.03423999999995, "r": 454.33505, "b": 428.16165, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Specifies that the row permission is to", "text": "Specifies that the row permission is to"}, {"self_ref": "#/texts/150", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 454.3461, "t": 436.03423999999995, "r": 527.05286, "b": 428.16165, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "be initially enabled", "text": "be initially enabled"}, {"self_ref": "#/texts/151", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 417.81711, "r": 185.17584, "b": 407.98697000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "ENABLE", "text": "ENABLE"}, {"self_ref": "#/texts/152", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 311.73431, "t": 415.34283, "r": 315.94684, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "S", "text": "S"}, {"self_ref": "#/texts/153", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": 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426.36832, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "i", "text": "i"}, {"self_ref": "#/texts/157", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 438.13208, "t": 415.34283, "r": 440.2268399999999, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "i", "text": "i"}, {"self_ref": "#/texts/158", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 445.88681, "t": 415.34283, "r": 448.95757999999995, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "t", "text": "t"}, {"self_ref": "#/texts/159", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 455.8532400000001, "t": 415.34283, "r": 460.67346000000003, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "b", "text": "b"}, {"self_ref": "#/texts/160", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 455.8848, "t": 415.34283, "r": 527.62122, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "be initially disabled", "text": "be initially disabled"}, {"self_ref": "#/texts/161", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 467.36746, "t": 415.34283, "r": 470.06998000000004, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "i", "text": "i"}, {"self_ref": "#/texts/162", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 472.73705999999993, "t": 415.34283, "r": 490.1676, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "iti ll", "text": "iti ll"}, {"self_ref": "#/texts/163", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 496.33661, "t": 415.34283, "r": 503.2608, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "di", "text": "di"}, {"self_ref": "#/texts/164", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 511.26138, "t": 415.34283, "r": 527.59674, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "bl d", "text": "bl d"}, {"self_ref": "#/texts/165", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 404.0228, "r": 187.6265, "b": 394.19265999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "DISABLE", "text": "DISABLE"}, {"self_ref": "#/texts/166", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 187.58514, "t": 404.0228, "r": 190.6628, "b": 394.20416000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ";", "text": ";"}, {"self_ref": "#/texts/167", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 352.0559997558594, "r": 215.37600708007812, "b": 340.95599365234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Column mask", "text": "Column mask", "level": 1}, {"self_ref": "#/texts/168", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 336.9112854003906, "r": 542.7664794921875, "b": 291.6988525390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 297]}], "orig": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number.", "text": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number."}, {"self_ref": "#/texts/169", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/170", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15"}, {"self_ref": "#/texts/171", "parent": {"cref": "#/body"}, "children": [], "label": "paragraph", "prov": [{"page_no": 11, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 412.20758056640625, "b": 711.2779541015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "Table 3-1 summarizes these special registers and their values.", "text": "Table 3-1 summarizes these special registers and their values."}, {"self_ref": "#/texts/172", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 698.501953125, "r": 372.6036376953125, "b": 690.177001953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "Table 3-1 Special registers and their corresponding values", "text": "Table 3-1 Special registers and their corresponding values"}, {"self_ref": "#/texts/173", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 577.5112915039062, "r": 538.493896484375, "b": 556.2984619140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:", "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"self_ref": "#/texts/174", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 548.471923828125, "r": 411.36138916015625, "b": 539.2589111328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "GLYPH<SM590000> A user connects to the server using the user profile ALICE.", "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/175", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 531.4921264648438, "r": 453.2580871582031, "b": 522.2791137695312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE.", "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/176", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 514.5123291015625, "r": 541.4498291015625, "b": 493.29949951171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 160]}], "orig": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/177", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 485.472900390625, "r": 547.2167358398438, "b": 452.2602844238281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 253]}], "orig": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/178", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.80101013183594, "t": 444.49346923828125, "r": 547.3540649414062, "b": 423.2806701660156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 133]}], "orig": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/179", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 195.2821044921875, "r": 341.2566223144531, "b": 186.95709228515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "Figure 3-5 Special registers and adopted authority", "text": "Figure 3-5 Special registers and adopted authority"}, {"self_ref": "#/texts/180", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 140.7323, "t": 405.01547, "r": 218.71170000000004, "b": 396.50473, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "SignedonasALICE Signed on as ALICE", "text": "SignedonasALICE Signed on as ALICE"}, {"self_ref": "#/texts/181", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 381.12558000000007, "r": 191.70256, "b": 372.61484, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/182", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 369.18066, "r": 232.56117, "b": 360.66992, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "CURRENT USER = ALICE", "text": "CURRENT USER = ALICE"}, {"self_ref": "#/texts/183", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 345.29076999999995, "r": 183.26944, "b": 336.78003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "CALL proc1", "text": "CALL proc1"}, {"self_ref": "#/texts/184", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 318.41476, "r": 184.17328, "b": 309.90402, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "P1 Proc1:", "text": "P1 Proc1:"}, {"self_ref": "#/texts/185", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 157.52185, "t": 306.46985, "r": 209.103, "b": 297.95911, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Owner = JOE", "text": "Owner = JOE"}, {"self_ref": "#/texts/186", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 157.52185, "t": 294.52493, "r": 281.68927, "b": 286.01419, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "SET OPTION USRPRF=*OWNER", "text": "SET OPTION USRPRF=*OWNER"}, {"self_ref": "#/texts/187", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 270.63507000000004, "r": 201.65666, "b": 262.12433, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/188", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 258.69016, "r": 234.57686999999999, "b": 250.17940999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "CURRENT USER = JOE", "text": "CURRENT USER = JOE"}, {"self_ref": "#/texts/189", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 225.84158000000002, "r": 191.70256, "b": 217.33083, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/190", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 213.89666999999997, "r": 232.56117, "b": 205.38590999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "CURRENT USER = ALICE", "text": "CURRENT USER = ALICE"}, {"self_ref": "#/texts/191", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 64.80000305175781, "t": 166.44528198242188, "r": 247.02536010742188, "b": 154.457275390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "3.2.2 Built-in global variables", "text": "3.2.2 Built-in global variables", "level": 1}, {"self_ref": "#/texts/192", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 140.29127502441406, "r": 518.0011596679688, "b": 119.0784683227539, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 161]}], "orig": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables.", "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"self_ref": "#/texts/193", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 106.27189636230469, "r": 532.3385009765625, "b": 73.05928039550781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 233]}], "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic.", "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"self_ref": "#/texts/194", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/195", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "19", "text": "19"}, {"self_ref": "#/texts/196", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "20", "text": "20"}, {"self_ref": "#/texts/197", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/198", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 342.5477294921875, "b": 711.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "Table 3-2 lists the nine built-in global variables.", "text": "Table 3-2 lists the nine built-in global variables."}, {"self_ref": "#/texts/199", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 698.501953125, "r": 201.1814727783203, "b": 690.177001953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Table 3-2 Built-in global variables", "text": "Table 3-2 Built-in global variables"}, {"self_ref": "#/texts/200", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 469.7992858886719, "r": 384.3638916015625, "b": 455.0362854003906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text": "3.3 VERIFY_GROUP_FOR_USER function", "level": 1}, {"self_ref": "#/texts/201", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8000030517578, "t": 437.4712829589844, "r": 547.2347412109375, "b": 356.2593994140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 576]}], "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error.", "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"self_ref": "#/texts/202", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 343.5125732421875, "r": 547.2573852539062, "b": 310.2999572753906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 235]}], "orig": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value.", "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"self_ref": "#/texts/203", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 297.4933776855469, "r": 458.44525146484375, "b": 288.2803955078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"self_ref": "#/texts/204", "parent": {"cref": "#/groups/8"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 280.45379638671875, "r": 406.0775146484375, "b": 271.2408142089844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "1. There are user profiles for MGR, JANE, JUDY, and TONY.", "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/205", "parent": {"cref": "#/groups/8"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 263.4739990234375, "r": 396.9881591796875, "b": 254.26100158691406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "2. The user profile JANE specifies a group profile of MGR.", "text": "2. The user profile JANE specifies a group profile of MGR.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/206", "parent": {"cref": "#/groups/8"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 246.4941864013672, "r": 536.568603515625, "b": 225.28138732910156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 127]}], "orig": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/207", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 12, "bbox": {"l": 151.20018005371094, "t": 217.305419921875, "r": 451.01605224609375, "b": 150.57144165039062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 265]}], "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"self_ref": "#/texts/208", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 720.341552734375, "r": 166.73934936523438, "b": 711.5667724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "RETURN", "text": "RETURN"}, {"self_ref": "#/texts/209", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 708.3417358398438, "r": 156.7793426513672, "b": 699.5669555664062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "CASE", "text": "CASE"}, {"self_ref": "#/texts/210", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 696.3419189453125, "r": 521.5742797851562, "b": 531.5695190429688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 437]}], "orig": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;", "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"self_ref": "#/texts/211", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 516.4940795898438, "r": 547.2122192382812, "b": 495.2812805175781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 136]}], "orig": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/212", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 487.51446533203125, "r": 469.1528015136719, "b": 478.3014831542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "-Human Resources can see the unmasked TAX_ID of the employees.", "text": "-Human Resources can see the unmasked TAX_ID of the employees.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/213", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 470.4748840332031, "r": 403.95953369140625, "b": 461.26190185546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "-Employees can see only their own unmasked TAX_ID.", "text": "-Employees can see only their own unmasked TAX_ID.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/214", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 453.4950866699219, "r": 545.16845703125, "b": 432.28228759765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 129]}], "orig": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/215", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 424.5154724121094, "r": 529.463623046875, "b": 415.302490234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/216", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 151.1997833251953, "t": 407.47589111328125, "r": 530.060302734375, "b": 398.2629089355469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/217", "parent": {"cref": "#/body"}, "children": [], "label": "paragraph", "prov": [{"page_no": 13, "bbox": {"l": 136.8000030517578, "t": 385.48199462890625, "r": 351.9873046875, "b": 377.156982421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"self_ref": "#/texts/218", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 13, "bbox": {"l": 136.8000030517578, "t": 368.3218994140625, "r": 526.5546875, "b": 107.55116271972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 590]}], "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;", "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}, {"self_ref": "#/texts/219", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/220", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "27", "text": "27"}, {"self_ref": "#/texts/221", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 449.952392578125, "b": 711.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/222", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 618.4619750976562, "r": 293.1380920410156, "b": 610.1370239257812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Figure 3-10 Column masks shown in System i Navigator", "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"self_ref": "#/texts/223", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 589.6253051757812, "r": 203.98521423339844, "b": 577.6372680664062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "3.6.6 Activating RCAC", "text": "3.6.6 Activating RCAC", "level": 1}, {"self_ref": "#/texts/224", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 563.4713134765625, "r": 547.2256469726562, "b": 530.2586669921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 265]}], "orig": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/groups/11"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 522.4918823242188, "r": 409.4788818359375, "b": 513.2788696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "1. Run the SQL statements that are shown in Example 3-10.", "text": "1. Run the SQL statements that are shown in Example 3-10.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/226", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 500.4420166015625, "r": 375.2909851074219, "b": 492.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table", "text": "Example 3-10 Activating RCAC on the EMPLOYEES table", "level": 1}, {"self_ref": "#/texts/227", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 483.3418884277344, "r": 376.6766052246094, "b": 474.5671081542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "/* Active Row Access Control (permissions) */", "text": "/* Active Row Access Control (permissions) */", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/228", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 471.3420715332031, "r": 354.86962890625, "b": 462.5672912597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "/* Active Column Access Control (masks)", "text": "/* Active Column Access Control (masks)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/229", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 365.77313232421875, "t": 471.3420715332031, "r": 376.6766052246094, "b": 462.5672912597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "*/", "text": "*/"}, {"self_ref": "#/texts/230", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 459.3422546386719, "r": 291.7178039550781, "b": 450.5674743652344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"self_ref": "#/texts/231", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 447.3424377441406, "r": 271.6783142089844, "b": 438.5676574707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "ACTIVATE ROW ACCESS CONTROL", "text": "ACTIVATE ROW ACCESS CONTROL"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 435.3426208496094, "r": 291.7178039550781, "b": 426.5678405761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"self_ref": "#/texts/233", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 411.4924011230469, "r": 540.8014526367188, "b": 378.27978515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/234", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 142.9621124267578, "r": 347.4305419921875, "b": 134.63710021972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "28", "text": "28"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 15, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 514.048583984375, "b": 687.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 228]}], "orig": "2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause.", "text": "2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 136.8000030517578, "t": 311.4420166015625, "r": 327.0932922363281, "b": 303.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 44]}], "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text": "Figure 4-68 Visual Explain with RCAC enabled"}, {"self_ref": "#/texts/239", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 15, "bbox": {"l": 136.8000030517578, "t": 285.4313659667969, "r": 547.2394409179688, "b": 252.21875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 232]}], "orig": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause.", "text": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 64.80000305175781, "t": 124.48210144042969, "r": 227.1014862060547, "b": 116.15709686279297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Figure 4-69 Index advice with no RCAC", "text": "Figure 4-69 Index advice with no RCAC"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 214.8000030517578, "t": 36.461997985839844, "r": 523.5935668945312, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example", "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}, {"self_ref": "#/texts/242", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "77", "text": "77"}, {"self_ref": "#/texts/243", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 16, "bbox": {"l": 64.80030822753906, "t": 720.3270263671875, "r": 500.697265625, "b": 85.39237976074219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1998]}], "orig": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;", "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}, {"self_ref": "#/texts/244", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 83.98200225830078, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "124", "text": "124"}, {"self_ref": "#/texts/245", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 98.94000244140625, "t": 36.461997985839844, "r": 339.819580078125, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 287.2200012207031, "t": 763.4519653320312, "r": 414.24481201171875, "b": 741.251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Back cover", "text": "Back cover"}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 18, "bbox": {"l": 27.0, "t": 718.3619995117188, "r": 447.3600158691406, "b": 651.5399780273438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i", "level": 1}, {"self_ref": "#/texts/248", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 549.8280029296875, "r": 127.443603515625, "b": 525.1680297851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Implement roles and separation of duties", "text": "Implement roles and separation of duties"}, {"self_ref": "#/texts/249", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 507.8280334472656, "r": 120.283203125, "b": 469.1280212402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Leverage row permissions on the database", "text": "Leverage row permissions on the database"}, {"self_ref": "#/texts/250", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 451.8480224609375, "r": 121.44960021972656, "b": 413.14801025390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Protect columns by defining column masks", "text": "Protect columns by defining column masks"}, {"self_ref": "#/texts/251", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 152.94000244140625, "t": 549.2714233398438, "r": 414.084228515625, "b": 468.4081115722656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 464]}], "orig": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "text": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"self_ref": "#/texts/252", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 152.9400177001953, "t": 460.292724609375, "r": 414.173828125, "b": 403.4290466308594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"self_ref": "#/texts/253", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 18, "bbox": {"l": 171.0, "t": 160.66200256347656, "r": 231.8876953125, "b": 152.3369903564453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "REDP-5110-00", "text": "REDP-5110-00"}, {"self_ref": "#/texts/254", "parent": {"cref": "#/pictures/15"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 558.11987, "t": 746.5313100000001, "r": 565.46039, "b": 737.3183, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "fi", "text": "fi"}, {"self_ref": "#/texts/255", "parent": {"cref": "#/pictures/16"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 474.60001, "t": 627.94342, "r": 580.88989, "b": 603.05902, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Redpaper", "text": "Redpaper"}, {"self_ref": "#/texts/256", "parent": {"cref": "#/pictures/16"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 582.53992, "t": 619.67285, "r": 592.13989, "b": 610.79285, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "\u2122", "text": "\u2122"}, {"self_ref": "#/texts/257", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 544.2816772460938, "r": 559.809326171875, "b": 489.8393859863281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 44]}], "orig": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION", "text": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION"}, {"self_ref": "#/texts/258", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 440.2080078125, "r": 587.38916015625, "b": 405.52801513671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE", "text": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE"}, {"self_ref": "#/texts/259", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 392.13970947265625, "r": 587.5205078125, "b": 250.36593627929688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 323]}], "orig": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment.", "text": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment."}, {"self_ref": "#/texts/260", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 213.1680908203125, "r": 570.947998046875, "b": 190.48809814453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "For more information: ibm.com /redbooks", "text": "For more information: ibm.com /redbooks"}], "pictures": [{"self_ref": "#/pictures/0", "parent": {"cref": "#/body"}, "children": [], "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 513.4560546875, "t": 765.9149169921875, "r": 586.1583251953125, "b": 737.1808471679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [], "references": [], "footnotes": [], "image": null, "annotations": []}, {"self_ref": "#/pictures/1", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/2"}, {"cref": "#/texts/3"}, {"cref": 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Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6468505859375, "t": 529.3934326171875, "r": 547.1978149414062, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808044433594, "t": 517.3936157226562, "r": 549.8472290039062, "b": 508.18060302734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 504.85394287109375, "r": 536.1293334960938, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6611328125, "t": 504.85394287109375, "r": 547.19287109375, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 492.3740539550781, "r": 549.8472290039062, "b": 483.16107177734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 479.8941650390625, "r": 536.0551147460938, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015014648438, "t": 479.8941650390625, "r": 547.14794921875, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 467.3545227050781, "r": 536.080078125, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.635498046875, "t": 467.3545227050781, "r": 547.19091796875, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.7970428466797, "t": 444.8548889160156, "r": 536.0908813476562, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.642822265625, "t": 444.8548889160156, "r": 547.1947631835938, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.7970428466797, "t": 432.8550720214844, "r": 536.1271362304688, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, 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Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0916748046875, "t": 310.3968200683594, "r": 547.19580078125, "b": 301.183837890625, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79702758789062, "t": 298.3970031738281, "r": 530.4808959960938, "b": 289.18402099609375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 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access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.1066284179688, "t": 98.36038970947266, "r": 547.169677734375, "b": 89.14738464355469, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "29", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717407226562, "t": 85.88050842285156, "r": 530.436279296875, "b": 76.6675033569336, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 42, "end_row_offset_idx": 43, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.9984741210938, "t": 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79901123046875, "t": 646.8715209960938, "r": 189.86537170410156, "b": 637.6585083007812, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Trademarks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 195.3968505859375, "t": 646.8715209960938, "r": 547.182861328125, "b": 637.6585083007812, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, 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"Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79803466796875, "t": 576.852783203125, "r": 339.18292236328125, "b": 567.6397705078125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.714111328125, "t": 576.852783203125, "r": 547.1387939453125, "b": 567.6397705078125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, 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"end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79806518554688, "t": 551.89306640625, "r": 284.0286560058594, "b": 542.6800537109375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449462890625, "t": 551.89306640625, "r": 547.1211547851562, "b": 542.6800537109375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79806518554688, "t": 529.3934326171875, "r": 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Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6468505859375, "t": 529.3934326171875, "r": 547.1978149414062, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79808044433594, "t": 517.3936157226562, "r": 549.8472290039062, "b": 508.18060302734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79806518554688, "t": 504.85394287109375, "r": 536.1293334960938, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6611328125, "t": 504.85394287109375, "r": 547.19287109375, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79806518554688, "t": 492.3740539550781, "r": 549.8472290039062, "b": 483.16107177734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 151.19720458984375, "t": 479.8941650390625, "r": 536.0551147460938, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015014648438, "t": 479.8941650390625, "r": 547.14794921875, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 151.19720458984375, "t": 467.3545227050781, "r": 536.080078125, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.635498046875, "t": 467.3545227050781, "r": 547.19091796875, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.7970428466797, "t": 444.8548889160156, "r": 536.0908813476562, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.642822265625, "t": 444.8548889160156, "r": 547.1947631835938, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.7970428466797, "t": 432.8550720214844, "r": 536.1271362304688, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, 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diff --git a/tests/data/groundtruth/docling_v2/redp5110_sampled.md b/tests/data/groundtruth/docling_v2/redp5110_sampled.md
index 8d2010d1..6480cec2 100644
--- a/tests/data/groundtruth/docling_v2/redp5110_sampled.md
+++ b/tests/data/groundtruth/docling_v2/redp5110_sampled.md
@@ -10,50 +10,50 @@ Front cover
 
 ## Contents
 
-| Notices                                                                                                                                        | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii   |
-|------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------|
-| Trademarks                                                                                                                                     | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii          |
-| DB2 for i Center of Excellence                                                                                                                 | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix                                          |
-| Preface                                                                                                                                        | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi    |
-| Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi |                                                                                                                                         |
-| Now you can become a published author, too!                                                                                                    | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii                                                                |
-| Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                  | xiii                                                                                                                                    |
-| Stay connected to IBM Redbooks                                                                                                                 | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv                                             |
-| Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                     | 1                                                                                                                                       |
-| 1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2               |                                                                                                                                         |
-| 1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                  | 2                                                                                                                                       |
-| 1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3              |                                                                                                                                         |
-| 1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                            | 4                                                                                                                                       |
-| 1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .                                         | 5                                                                                                                                       |
-| Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                              | 7                                                                                                                                       |
-| 2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .      | 8                                                                                                                                       |
-| 2.1.1 DDM and DRDA application server access: QIBM\_DB\_DDMDRDA . . . . . . . . . . .                                                            | 8                                                                                                                                       |
-| 2.1.2 Toolbox application server access: QIBM\_DB\_ZDA. . . . . . . . . . . . . . . . . . . . . . . .                                            | 8                                                                                                                                       |
-| 2.1.3 Database Administrator function: QIBM\_DB\_SQLADM . . . . . . . . . . . . . . . . . . . . .                                                | 9                                                                                                                                       |
-| 2.1.4 Database Information function: QIBM\_DB\_SYSMON                                                                                            | . . . . . . . . . . . . . . . . . . . . . . 9                                                                                           |
-| 2.1.5 Security Administrator function: QIBM\_DB\_SECADM . . . . . . . . . . . . . . . . . . . . . .                                              | 9                                                                                                                                       |
-| 2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                       | 10                                                                                                                                      |
-| 2.1.7 Verifying function usage IDs for RCAC with the FUNCTION\_USAGE view . . . . .                                                             | 10                                                                                                                                      |
-| 2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10              |                                                                                                                                         |
-| Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                     | 13                                                                                                                                      |
-| 3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .                                        | 14                                                                                                                                      |
-| 3.1.1 Row permission and column mask definitions                                                                                               | . . . . . . . . . . . . . . . . . . . . . . . . . . . 14                                                                                |
-| 3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                               | 16                                                                                                                                      |
-| 3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                            | 18                                                                                                                                      |
-| 3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                    | 18                                                                                                                                      |
-| 3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                      | 19                                                                                                                                      |
-| 3.3 VERIFY\_GROUP\_FOR\_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                     | 20                                                                                                                                      |
-| 3.4 Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .                                           | 21                                                                                                                                      |
-| 3.5 SELECT, INSERT, and UPDATE behavior with RCAC                                                                                              | . . . . . . . . . . . . . . . . . . . . . . . . 22                                                                                      |
-| 3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                        | 22                                                                                                                                      |
-| 3.6.1 Assigning the QIBM\_DB\_SECADM function ID to the consultants. . . . . . . . . . . .                                                       | 23                                                                                                                                      |
-| 3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .                                      | 23                                                                                                                                      |
-| 3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                       | 24                                                                                                                                      |
-| 3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                  | 25                                                                                                                                      |
-| 3.6.5 Defining and creating column masks                                                                                                       | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26                                                                  |
-| 3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                      | 28                                                                                                                                      |
-| 3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                      | 29                                                                                                                                      |
-| 3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .                                             | 32                                                                                                                                      |
+| Notices                                                                                                                                                                   | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii   |
+|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------|
+| Trademarks                                                                                                                                                                | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii          |
+| DB2 for i Center of Excellence                                                                                                                                            | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix                                          |
+| Preface                                                                                                                                                                   | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi    |
+| Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi                            |                                                                                                                                         |
+| Now you can become a published author, too!                                                                                                                               | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii                                                                |
+| Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                             | xiii                                                                                                                                    |
+| Stay connected to IBM Redbooks                                                                                                                                            | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv                                             |
+| Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                                | 1                                                                                                                                       |
+| 1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2                                          |                                                                                                                                         |
+| 1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                             | 2                                                                                                                                       |
+| 1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3                                         |                                                                                                                                         |
+| 1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                       | 4                                                                                                                                       |
+| 1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .                                                                    | 5                                                                                                                                       |
+| Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                         | 7                                                                                                                                       |
+| 2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                 | 8                                                                                                                                       |
+| 2.1.1 DDM and DRDA application server access: QIBM\_DB\_DDMDRDA . . . . . . . . . . .                                                                                       | 8                                                                                                                                       |
+| 2.1.2 Toolbox application server access: QIBM\_DB\_ZDA. . . . . . . . . . . . . . . . . . . . . . . .                                                                       | 8                                                                                                                                       |
+| 2.1.3 Database Administrator function: QIBM\_DB\_SQLADM . . . . . . . . . . . . . . . . . . . . .                                                                           | 9                                                                                                                                       |
+| 2.1.4 Database Information function: QIBM\_DB\_SYSMON                                                                                                                       | . . . . . . . . . . . . . . . . . . . . . . 9                                                                                           |
+| 2.1.5 Security Administrator function: QIBM\_DB\_SECADM . . . . . . . . . . . . . . . . . . . . . .                                                                         | 9                                                                                                                                       |
+| 2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                                  | 10                                                                                                                                      |
+| 2.1.7 Verifying function usage IDs for RCAC with the FUNCTION\_USAGE view . . . . .                                                                                        | 10                                                                                                                                      |
+| 2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10                                         |                                                                                                                                         |
+| Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                                | 13                                                                                                                                      |
+| 3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .                                                                   | 14                                                                                                                                      |
+| 3.1.1 Row permission and column mask definitions                                                                                                                          | . . . . . . . . . . . . . . . . . . . . . . . . . . . 14                                                                                |
+| 3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                          | 16                                                                                                                                      |
+| 3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                       | 18                                                                                                                                      |
+| 3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                               | 18                                                                                                                                      |
+| 3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                 | 19                                                                                                                                      |
+| 3.3 VERIFY\_GROUP\_FOR\_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                                | 20                                                                                                                                      |
+| 3.4 Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .                                                                      | 21                                                                                                                                      |
+| . . . . . . . . . . . . . . . . . . . . . . . .                                                                                                                           | 22                                                                                                                                      |
+| 3.5 SELECT, INSERT, and UPDATE behavior with RCAC 3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 22                                                                                                                                      |
+| 3.6.1 Assigning the QIBM\_DB\_SECADM function ID to the consultants. . . . . . . . . . . .                                                                                  | 23                                                                                                                                      |
+| 3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .                                                                 | 23                                                                                                                                      |
+| 3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                                  | 24                                                                                                                                      |
+| 3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                             | 25                                                                                                                                      |
+| 3.6.5 Defining and creating column masks                                                                                                                                  | . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26                                                                  |
+| 3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                 | 28                                                                                                                                      |
+| 3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                                 | 29                                                                                                                                      |
+| 3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .                                                                        | 32                                                                                                                                      |
 
 DB2 for i Center of Excellence
 
@@ -238,20 +238,20 @@ Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL aut
 
 Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority
 
-| User action                                                                    | *JOBCTL   | QIBM\_DB\_SECADM   | QIBM\_DB\_SQLADM   | QIBM\_DB\_SYSMON No Authority   |
-|--------------------------------------------------------------------------------|-----------|------------------|------------------|-------------------------------|
-| SET CURRENT DEGREE  (SQL statement)                                            | X         |                  | X                |                               |
-| CHGQRYA  command targeting a different user's job                              | X         |                  | X                |                               |
-| STRDBMON  or  ENDDBMON  commands targeting a different user's job              | X         |                  | X                |                               |
-| STRDBMON  or  ENDDBMON  commands targeting a job that matches the current user | X         |                  | X                | X X                           |
-| QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job          | X         |                  | X                | X                             |
-| Visual Explain within Run SQL scripts                                          | X         |                  | X                | X X                           |
-| Visual Explain outside of Run SQL scripts                                      | X         |                  | X                |                               |
-| ANALYZE PLAN CACHE procedure                                                   | X         |                  | X                |                               |
-| DUMP PLAN CACHE procedure                                                      | X         |                  | X                |                               |
-| MODIFY PLAN CACHE procedure                                                    | X         |                  | X                |                               |
-| MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority)    | X         |                  | X                |                               |
-| CHANGE PLAN CACHE SIZE procedure (currently does not check authority)          | X         |                  | X                |                               |
+| User action                                                                    | *JOBCTL   | QIBM\_DB\_SECADM   | QIBM\_DB\_SQLADM   | QIBM\_DB\_SYSMON   | No Authority   |
+|--------------------------------------------------------------------------------|-----------|------------------|------------------|------------------|----------------|
+| SET CURRENT DEGREE  (SQL statement)                                            | X         |                  | X                |                  |                |
+| CHGQRYA  command targeting a different user's job                              | X         |                  | X                |                  |                |
+| STRDBMON  or  ENDDBMON  commands targeting a different user's job              | X         |                  | X                |                  |                |
+| STRDBMON  or  ENDDBMON  commands targeting a job that matches the current user | X         |                  | X                | X                | X              |
+| QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job          | X         |                  | X                | X                |                |
+| Visual Explain within Run SQL scripts                                          | X         |                  | X                | X                | X              |
+| Visual Explain outside of Run SQL scripts                                      | X         |                  | X                |                  |                |
+| ANALYZE PLAN CACHE procedure                                                   | X         |                  | X                |                  |                |
+| DUMP PLAN CACHE procedure                                                      | X         |                  | X                |                  |                |
+| MODIFY PLAN CACHE procedure                                                    | X         |                  | X                |                  |                |
+| MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority)    | X         |                  | X                |                  |                |
+| CHANGE PLAN CACHE SIZE procedure (currently does not check authority)          | X         |                  | X                |                  |                |
 
 The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.Figure 3-1 CREATE PERMISSION SQL statement
 
diff --git a/tests/data/groundtruth/docling_v2/redp5110_sampled.pages.json b/tests/data/groundtruth/docling_v2/redp5110_sampled.pages.json
index a2dea0f8..43a46879 100644
--- a/tests/data/groundtruth/docling_v2/redp5110_sampled.pages.json
+++ b/tests/data/groundtruth/docling_v2/redp5110_sampled.pages.json
@@ -1 +1 @@
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2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3.6.2", "bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 173.32271, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8541, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "23", "bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "3.6.3", "bbox": {"l": 151.19717, "t": 643.66034, "r": 173.32227, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.85353, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "24", "bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "3.6.4", "bbox": {"l": 151.19717, "t": 656.14021, "r": 173.35289, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89182, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "25", "bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "3.6.5", "bbox": {"l": 151.19717, "t": 668.62009, "r": 173.35289, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Defining and creating column masks", "bbox": {"l": 178.89182, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.98996, "t": 668.62009, "r": 530.54413, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "26", "bbox": {"l": 536.08301, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "3.6.6", "bbox": {"l": 151.19717, "t": 681.15973, "r": 173.38359, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.93019, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "28", "bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3.6.7", "bbox": {"l": 151.19717, "t": 693.63961, "r": 173.32332, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Demonstrating data access with RCAC", "bbox": {"l": 178.85486, "t": 693.63961, "r": 350.80011, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 356.33163, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "29", "bbox": {"l": 536.10663, "t": 693.63961, "r": 547.16968, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "3.6.8", "bbox": {"l": 151.19717, "t": 706.119492, "r": 173.44592, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00812, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "32", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "document_index", "bbox": {"l": 136.79701, "t": 132.64862000000005, "r": 549.84723, "b": 715.332497, "coord_origin": "TOPLEFT"}, "confidence": 0.9803490042686462, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3.6.2", "bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 173.32271, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8541, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "23", "bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "3.6.3", "bbox": {"l": 151.19717, "t": 643.66034, "r": 173.32227, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.85353, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "24", "bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "3.6.4", "bbox": {"l": 151.19717, "t": 656.14021, "r": 173.35289, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89182, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "25", "bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "3.6.5", "bbox": {"l": 151.19717, "t": 668.62009, "r": 173.35289, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Defining and creating column masks", "bbox": {"l": 178.89182, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.98996, "t": 668.62009, "r": 530.54413, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "26", "bbox": {"l": 536.08301, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "3.6.6", "bbox": {"l": 151.19717, "t": 681.15973, "r": 173.38359, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.93019, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "28", "bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3.6.7", "bbox": {"l": 151.19717, "t": 693.63961, "r": 173.32332, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Demonstrating data access with RCAC", "bbox": {"l": 178.85486, "t": 693.63961, "r": 350.80011, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 356.33163, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "29", "bbox": {"l": 536.10663, "t": 693.63961, "r": 547.16968, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "3.6.8", "bbox": {"l": 151.19717, "t": 706.119492, "r": 173.44592, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00812, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "32", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "children": [{"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 195.39685, "t": 145.12847999999997, 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with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00812, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 128, "label": "text", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 127, "text": "32", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 3, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.892595112323761, "cells": [{"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9473134279251099, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.79701, "t": 132.64862000000005, "r": 549.84723, "b": 715.332497, "coord_origin": "TOPLEFT"}, "confidence": 0.9803490042686462, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, 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"TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and 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1, "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0882, "t": 643.66034, "r": 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false, "row_section": false}]}}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Contents"}, {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.79701, "t": 132.64862000000005, "r": 549.84723, "b": 715.332497, "coord_origin": "TOPLEFT"}, "confidence": 0.9803490042686462, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3.6.2", "bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 173.32271, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8541, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "23", "bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "3.6.3", "bbox": {"l": 151.19717, "t": 643.66034, "r": 173.32227, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.85353, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "24", 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"b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 30, "end_row_offset_idx": 31, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 30, "end_row_offset_idx": 31, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "19", "column_header": false, "row_header": false, 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"row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . 22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79701, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, 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1, "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "24", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "25", "column_header": false, "row_header": false, 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"end_col_offset_idx": 1, "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "28", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10663, "t": 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false, "row_section": false}]}, {"label": "page_footer", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.892595112323761, "cells": [{"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "iii"}, {"label": "page_footer", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9473134279251099, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}], "body": [{"label": "section_header", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Contents"}, {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.79701, "t": 132.64862000000005, "r": 549.84723, "b": 715.332497, "coord_origin": "TOPLEFT"}, "confidence": 0.9803490042686462, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 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. . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Preface", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 542.91888, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "DB2fi for i Row and Column Access Control (RCAC). 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A solid background in IBM i object level security, DB2 for i relational ", "bbox": {"l": 136.79987, "t": 238.60712, "r": 521.25488, "b": 247.82012999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "database concepts, and SQL is assumed.", "bbox": {"l": 136.79987, "t": 250.60693000000003, "r": 321.69434, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. 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He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. 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His areas of expertise are database technology, ", "bbox": {"l": 263.3996, "t": 647.62454, "r": 524.77386, "b": 656.83754, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "performance, and data warehousing. Hernando can be ", "bbox": {"l": 263.3996, "t": 659.62434, "r": 508.27124, "b": 668.83735, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "contacted at ", "bbox": {"l": 263.3996, "t": 671.62415, "r": 320.63568, "b": 680.83716, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "hbedoya@us.ibm.com", "bbox": {"l": 320.63971, "t": 671.77356, "r": 410.57852, "b": 680.54832, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": ".", "bbox": {"l": 410.5795, "t": 671.62415, "r": 413.34839, "b": 680.83716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master\u2019s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"label": "page_footer", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9040942788124084, "cells": [{"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "xi"}, {"label": "page_footer", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469243884086609, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}], "body": [{"label": "section_header", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9174709916114807, "cells": [{"id": 2, "text": "Preface", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preface"}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79984, "t": 132.64862000000005, "r": 547.30823, "b": 201.86072000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9869155883789062, "cells": [{"id": 3, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 542.91888, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "bbox": {"l": 136.79984, "t": 144.64844000000005, "r": 526.65509, "b": 153.86145, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "bbox": {"l": 136.79984, "t": 156.64824999999996, "r": 536.82135, "b": 165.86127, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "bbox": {"l": 136.79987, "t": 168.64806999999996, "r": 544.67975, "b": 177.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "of defining, creating, and implementing the row permissions and column masks in a relational ", "bbox": {"l": 136.79987, "t": 180.64788999999996, "r": 547.30823, "b": 189.86090000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "database environment.", "bbox": {"l": 136.79987, "t": 192.64770999999996, "r": 238.32117, "b": 201.86072000000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79987, "t": 214.60748, "r": 546.4657, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9872201681137085, "cells": [{"id": 9, "text": "This paper is intended for database engineers, data-centric application developers, and ", "bbox": {"l": 136.79987, "t": 214.60748, "r": 524.18518, "b": 223.82050000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security officers who want to design and implement RCAC as a part of their data control and ", "bbox": {"l": 136.79987, "t": 226.6073, "r": 546.4657, "b": 235.82030999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "bbox": {"l": 136.79987, "t": 238.60712, "r": 521.25488, "b": 247.82012999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "database concepts, and SQL is assumed.", "bbox": {"l": 136.79987, "t": 250.60693000000003, "r": 321.69434, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"label": "section_header", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}, "confidence": 0.9255505204200745, "cells": [{"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Authors"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 341.84152, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318943977356, "cells": [{"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"label": "picture", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 142.52883911132812, "t": 375.0449523925781, "r": 251.47850036621094, "b": 503.20648193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9862572550773621, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 263.39957, "t": 375.64877, "r": 541.25079, "b": 516.85974, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "bbox": {"l": 263.3996, "t": 459.64749, "r": 528.91016, "b": 468.86047, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "numerous technical roles, including independent software ", "bbox": {"l": 263.3996, "t": 471.64731, "r": 520.24207, "b": 480.86029, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "vendors technical support on a broad range of IBM ", "bbox": {"l": 263.3996, "t": 483.64713, "r": 490.6967200000001, "b": 492.86011, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "technologies and products, and supporting customers in the ", "bbox": {"l": 263.3996, "t": 495.64694, "r": 530.95514, "b": 504.85992, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "IBM Executive Briefing Center and IBM Project Office.", "bbox": {"l": 263.3996, "t": 507.64676, "r": 501.62973, "b": 516.85974, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"label": "picture", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 145.41445922851562, "t": 527.2447509765625, "r": 252.08840942382812, "b": 635.383056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.987165629863739, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 263.3996, "t": 527.62653, "r": 541.27374, "b": 680.83716, "coord_origin": "TOPLEFT"}, "confidence": 0.9823779463768005, "cells": [{"id": 29, "text": "Hernando Bedoya", "bbox": {"l": 263.3996, "t": 527.62653, "r": 348.38229, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": " is a Senior IT Specialist at STG Lab ", "bbox": {"l": 348.41916, "t": 527.62653, "r": 512.3429, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Services and Training in Rochester, Minnesota. He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. His areas of expertise are database technology, ", "bbox": {"l": 263.3996, "t": 647.62454, "r": 524.77386, "b": 656.83754, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "performance, and data warehousing. Hernando can be ", "bbox": {"l": 263.3996, "t": 659.62434, "r": 508.27124, "b": 668.83735, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "contacted at ", "bbox": {"l": 263.3996, "t": 671.62415, "r": 320.63568, "b": 680.83716, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "hbedoya@us.ibm.com", "bbox": {"l": 320.63971, "t": 671.77356, "r": 410.57852, "b": 680.54832, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": ".", "bbox": {"l": 410.5795, "t": 671.62415, "r": 413.34839, "b": 680.83716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master\u2019s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}], "headers": [{"label": "page_footer", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9040942788124084, "cells": [{"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "xi"}, {"label": "page_footer", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469243884086609, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "1", "bbox": {"l": 541.67987, "t": 754.848721, "r": 547.21765, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Chapter 1.", "bbox": {"l": 81.0, "t": 268.54272000000003, "r": 115.13253, "b": 274.98071000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Securing and protecting IBM DB2 ", "bbox": {"l": 136.8, "t": 254.88635, "r": 547.30475, "b": 278.91785000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "data", "bbox": {"l": 136.8, "t": 285.84671, "r": 190.29802, "b": 309.8782, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "bbox": {"l": 136.8, "t": 348.70871, "r": 542.25665, "b": 357.92169, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 ", "bbox": {"l": 136.80096, "t": 360.70853, "r": 544.96643, "b": 369.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "bbox": {"l": 136.79965, "t": 372.70853, "r": 529.53839, "b": 381.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ ", "bbox": {"l": 136.79965, "t": 384.7083400000001, "r": 535.32874, "b": 393.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "bbox": {"l": 136.80026, "t": 396.70853, "r": 521.64374, "b": 405.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "bbox": {"l": 136.80026, "t": 408.7083400000001, "r": 547.13135, "b": 417.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "bbox": {"l": 136.80023, "t": 420.70816, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 525.06482, "b": 451.9407, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "bbox": {"l": 136.80025, "t": 454.72754000000003, "r": 518.26825, "b": 463.94052, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "industries, securing the data is required by law and subject to audits. 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All rights reserved."}], "body": [{"label": "picture", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "picture", "bbox": {"l": 32.05510711669922, "t": 70.42633819580078, "r": 239.62696838378906, "b": 238.0409698486328, "coord_origin": "TOPLEFT"}, "confidence": 0.7604207992553711, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 500.39999, "t": 93.16870000000006, "r": 522.61774, "b": 130.13171, "coord_origin": "TOPLEFT"}, "confidence": 0.7054201364517212, "cells": [{"id": 24, "text": "1", "bbox": {"l": 500.39999, "t": 93.16870000000006, "r": 522.61774, "b": 130.13171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}, {"label": "section_header", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 136.8, "t": 254.88635, "r": 547.30475, "b": 309.8782, "coord_origin": "TOPLEFT"}, "confidence": 0.9377050399780273, "cells": [{"id": 3, "text": "Securing and protecting IBM DB2 ", "bbox": {"l": 136.8, "t": 254.88635, "r": 547.30475, "b": 278.91785000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "data", "bbox": {"l": 136.8, "t": 285.84671, "r": 190.29802, "b": 309.8782, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Securing and protecting IBM DB2 data"}, {"label": "text", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "text", "bbox": {"l": 81.0, "t": 268.54272000000003, "r": 115.13253, "b": 274.98071000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "Chapter 1.", "bbox": {"l": 81.0, "t": 268.54272000000003, "r": 115.13253, "b": 274.98071000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 1."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79965, "t": 348.70871, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}, "confidence": 0.9868757724761963, "cells": [{"id": 5, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "bbox": {"l": 136.8, "t": 348.70871, "r": 542.25665, "b": 357.92169, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 ", "bbox": {"l": 136.80096, "t": 360.70853, "r": 544.96643, "b": 369.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "bbox": {"l": 136.79965, "t": 372.70853, "r": 529.53839, "b": 381.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ ", "bbox": {"l": 136.79965, "t": 384.7083400000001, "r": 535.32874, "b": 393.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "bbox": {"l": 136.80026, "t": 396.70853, "r": 521.64374, "b": 405.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "bbox": {"l": 136.80026, "t": 408.7083400000001, "r": 547.13135, "b": 417.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "bbox": {"l": 136.80023, "t": 420.70816, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 527.2063, "b": 487.94016, "coord_origin": "TOPLEFT"}, "confidence": 0.9865864515304565, "cells": [{"id": 12, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 525.06482, "b": 451.9407, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "bbox": {"l": 136.80025, "t": 454.72754000000003, "r": 518.26825, "b": 463.94052, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "bbox": {"l": 136.80025, "t": 466.72736, "r": 527.2063, "b": 475.94034, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "longer an option; it is a requirement.", "bbox": {"l": 136.80025, "t": 478.72717, "r": 296.31067, "b": 487.94016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80025, "t": 500.68698, "r": 547.15515, "b": 521.89978, "coord_origin": "TOPLEFT"}, "confidence": 0.9734498858451843, "cells": [{"id": 16, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "bbox": {"l": 136.80025, "t": 500.68698, "r": 547.15515, "b": 509.89996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "are covered in this chapter:", "bbox": {"l": 136.80025, "t": 512.6868, "r": 257.28036, "b": 521.89978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This chapter describes how you can secure and protect data in DB2 for i. 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. 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", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837489128112793, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621999263763428, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. 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Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> First, and most important, is the definition of a company\u2019s security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077319264411926, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. 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Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837489128112793, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621999263763428, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company\u2019s most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today\u2019s connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}, {"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889443874359131, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "section_header", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1 Security fundamentals"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9663435220718384, "cells": [{"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"label": "list_item", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.12487999999996, "r": 547.16425, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> First, and most important, is the definition of a company\u2019s security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077319264411926, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837489128112793, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621999263763428, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company\u2019s most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today\u2019s connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}], "headers": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889443874359131, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. 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Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858148097991943, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.9757603406906128, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838991165161133, "cells": [], "children": [{"id": 9, "label": "text", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.949161946773529, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. 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However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.9757603406906128, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"label": "picture", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838991165161133, "cells": [], "children": [{"id": 9, "label": "text", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1-2 Existing row and column controls"}, {"label": "page_footer", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_footer", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.949161946773529, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. 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Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"label": "section_header", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.3.1 Existing row and column control"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858148097991943, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. 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However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.9757603406906128, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], 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"cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_footer", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.949161946773529, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "2.2", "bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "VARCHAR(30)", "bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "DENIED: The user profile is not allowed to use the function.", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "USER_TYPE", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "VARCHAR(5)", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Type of user profile:", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "GROUP: The user profile is a group.", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.969738245010376, "cells": [{"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}, "confidence": 0.9551491737365723, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410472512245178, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417492747306824, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.95621657371521, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682287573814392, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056942462921143, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636377096176147, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737266302108765, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197836518287659, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 142.79999, "t": 296.5379899999999, "r": 539.10712, "b": 435.84369, "coord_origin": "TOPLEFT"}, "confidence": 0.9851696491241455, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "VARCHAR(30)", "bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 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"TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417492747306824, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.95621657371521, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682287573814392, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056942462921143, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", 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Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197836518287659, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 142.79999, "t": 296.5379899999999, "r": 539.10712, "b": 435.84369, "coord_origin": "TOPLEFT"}, "confidence": 0.9851696491241455, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, 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"confidence": 0.0, "cells": [{"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 49, "label": "text", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 52, "label": "text", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 71, "text": "GROUP: The user profile is a group.", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 51, "label": "text", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 70, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "ched", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl"], "num_rows": 5, "num_cols": 3, "table_cells": [{"bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Column name", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Data type", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Description", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "FUNCTION_ID", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(30)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "ID of the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_NAME", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(10)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 353.88333, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Name of the user profile that has a usage setting for this  function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USAGE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(7)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.41626, "t": 364.51862, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Usage setting: GLYPH<SM590000> ALLOWED: The user profile is allowed to use the function. GLYPH<SM590000> DENIED: The user profile is not allowed to use the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_TYPE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(5)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.43161, "t": 405.55865, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Type of user profile: GLYPH<SM590000> USER: The user profile is a user. GLYPH<SM590000> GROUP: The user profile is a group.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647642970085144, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8165044188499451, "cells": [{"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.65802, "r": 331.67731, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5808849930763245, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 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"coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 0.5631598830223083, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 32, "text": 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620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.54872, "r": 547.22345, "b": 709.760956, "coord_origin": "TOPLEFT"}, "confidence": 0.9853105545043945, "cells": [{"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9166075587272644, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9529877305030823, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.969738245010376, "cells": [{"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.6 Change Function Usage CL command"}, {"label": "text", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}, "confidence": 0.9551491737365723, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410472512245178, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417492747306824, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.95621657371521, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682287573814392, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056942462921143, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636377096176147, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737266302108765, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197836518287659, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 142.79999, "t": 296.5379899999999, "r": 539.10712, "b": 435.84369, "coord_origin": "TOPLEFT"}, "confidence": 0.9851696491241455, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, 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334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "DENIED: The user profile is not allowed to use the function.", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "USER_TYPE", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "VARCHAR(5)", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Type of user profile:", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": 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315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 36, "label": "text", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 40, "label": "text", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 45, "label": "text", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 64, "text": "DENIED: The user profile is not allowed to use the function.", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 63, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 46, "label": "text", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 65, "text": "USER_TYPE", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 47, "label": "text", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 66, "text": "VARCHAR(5)", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 48, "label": "text", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 67, "text": "Type of user profile:", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 50, "label": "text", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 49, "label": "text", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 52, "label": "text", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 71, "text": "GROUP: The user profile is a group.", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 51, "label": "text", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 70, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "ched", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl"], "num_rows": 5, "num_cols": 3, "table_cells": [{"bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Column name", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Data type", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Description", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "FUNCTION_ID", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(30)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "ID of the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_NAME", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(10)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 353.88333, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Name of the user profile that has a usage setting for this  function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USAGE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(7)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.41626, "t": 364.51862, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Usage setting: GLYPH<SM590000> ALLOWED: The user profile is allowed to use the function. GLYPH<SM590000> DENIED: The user profile is not allowed to use the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_TYPE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(5)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.43161, "t": 405.55865, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Type of user profile: GLYPH<SM590000> USER: The user profile is a user. GLYPH<SM590000> GROUP: The user profile is a group.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647642970085144, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8165044188499451, "cells": [{"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.65802, "r": 331.67731, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5808849930763245, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 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620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.54872, "r": 547.22345, "b": 709.760956, "coord_origin": "TOPLEFT"}, "confidence": 0.9853105545043945, "cells": [{"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}], "headers": [{"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9166075587272644, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9529877305030823, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 2. Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "11", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "bbox": {"l": 64.800003, "t": 383.5379899999999, "r": 391.75464, "b": 391.86301, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "User action", "bbox": {"l": 70.800301, "t": 400.51827999999995, "r": 119.78551, "b": 408.84329, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*JOBCTL", "bbox": {"l": 424.93805, "t": 447.52255, "r": 433.26297000000005, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "QIBM_DB_SECADM", "bbox": {"l": 450.13806, "t": 401.6000700000001, "r": 458.46298, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "QIBM_DB_SQLADM", "bbox": {"l": 475.93835000000007, "t": 401.53442, "r": 484.26327999999995, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "QIBM_DB_SYSMON", "bbox": {"l": 501.13837, "t": 401.6145, "r": 509.46329, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "No Authority", "bbox": {"l": 526.39862, "t": 432.79944, "r": 534.72357, "b": 487.02005, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "SET CURRENT DEGREE", "bbox": {"l": 70.800003, "t": 498.69299, "r": 151.6794, "b": 506.66699, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " (SQL statement)", "bbox": {"l": 151.6803, "t": 498.55798, "r": 220.15681000000004, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "X", "bbox": {"l": 429.0, "t": 498.55798, "r": 435.00299000000007, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 480.00031, "t": 498.55798, "r": 486.0033, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "CHGQRYA", "bbox": {"l": 70.800018, "t": 517.65329, "r": 102.23972, "b": 525.62729, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": " command targeting a different user\u2019s job", "bbox": {"l": 102.23972, "t": 517.51828, "r": 264.5538, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "X", "bbox": {"l": 429.00003, "t": 517.51828, "r": 435.00302000000005, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "X", "bbox": {"l": 480.00034, "t": 517.51828, "r": 486.00333, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "STRDBMON", "bbox": {"l": 70.800049, "t": 536.67299, "r": 106.73975, "b": 544.64699, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": " or ", "bbox": {"l": 106.73975, "t": 536.5379800000001, "r": 119.77895, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "ENDDBMON", "bbox": {"l": 119.69975000000001, "t": 536.67299, "r": 155.69974, "b": 544.64699, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": " commands targeting a different user\u2019s job", "bbox": {"l": 155.69974, "t": 536.5379800000001, "r": 322.50574, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "X", "bbox": {"l": 429.00003, "t": 536.5379800000001, "r": 435.00302000000005, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "X", "bbox": {"l": 480.00034, "t": 536.5379800000001, "r": 486.00333, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "STRDBMON", "bbox": {"l": 70.800049, "t": 555.69269, "r": 106.73975, "b": 563.66669, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": " or ", "bbox": {"l": 106.73975, "t": 555.55768, "r": 119.77895, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ENDDBMON", "bbox": {"l": 119.69975000000001, "t": 555.69269, "r": 155.69974, "b": 563.66669, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": " commands targeting a job that matches the current user", "bbox": {"l": 155.69974, "t": 555.55768, "r": 381.02185, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "X", "bbox": {"l": 429.00003, "t": 555.55768, "r": 435.00302000000005, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "X", "bbox": {"l": 480.00034, "t": 555.55768, "r": 486.00333, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "X", "bbox": {"l": 505.26061999999996, "t": 555.55768, "r": 511.26361, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "X", "bbox": {"l": 530.76031, "t": 555.55768, "r": 536.76331, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "QUSRJOBI() API format 900 or System i Navigator\u2019s SQL Details for Job", "bbox": {"l": 70.800049, "t": 574.51797, "r": 359.51736, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "X", "bbox": {"l": 429.0000600000001, "t": 574.51797, "r": 435.00305000000003, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "X", "bbox": {"l": 480.00037, "t": 574.51797, "r": 486.00335999999993, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "X", "bbox": {"l": 505.2606799999999, "t": 574.51797, "r": 511.26367, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Visual Explain within Run SQL scripts", "bbox": {"l": 70.800079, "t": 593.5376699999999, "r": 220.75178999999997, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "X", "bbox": {"l": 429.0000600000001, "t": 593.5376699999999, "r": 435.00305000000003, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "X", "bbox": {"l": 480.00037, "t": 593.5376699999999, "r": 486.00335999999993, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "X", "bbox": {"l": 505.2606799999999, "t": 593.5376699999999, "r": 511.26367, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "X", "bbox": {"l": 530.76038, "t": 593.5376699999999, "r": 536.76337, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "Visual Explain outside of Run SQL scripts", "bbox": {"l": 70.800079, "t": 612.55737, "r": 236.6548, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "X", "bbox": {"l": 429.0000600000001, "t": 612.55737, "r": 435.00305000000003, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "X", "bbox": {"l": 480.00037, "t": 612.55737, "r": 486.00335999999993, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "ANALYZE PLAN CACHE procedure", "bbox": {"l": 70.800079, "t": 631.51767, "r": 213.12968, "b": 639.84268, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "X", "bbox": {"l": 429.0000600000001, "t": 631.51767, "r": 435.00305000000003, "b": 639.84268, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "X", "bbox": {"l": 480.00037, "t": 631.51767, "r": 486.00335999999993, "b": 639.84268, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "DUMP PLAN CACHE procedure", "bbox": {"l": 70.800079, "t": 650.53737, "r": 199.87808, "b": 658.86238, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "X", "bbox": {"l": 429.0000600000001, "t": 650.53737, "r": 435.00305000000003, "b": 658.86238, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "X", "bbox": {"l": 480.00037, "t": 650.53737, "r": 486.00335999999993, "b": 658.86238, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "MODIFY PLAN CACHE procedure", "bbox": {"l": 70.800079, "t": 669.55708, "r": 208.36777, "b": 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"coord_origin": "TOPLEFT"}}, {"id": 77, "text": "X", "bbox": {"l": 429.0000600000001, "t": 707.537071, "r": 435.00305000000003, "b": 715.862068, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "X", "bbox": {"l": 480.00037, "t": 707.537071, "r": 486.00335999999993, "b": 715.862068, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 2, "label": "text", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9789126515388489, "cells": [{"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665539860725403, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "caption", "bbox": {"l": 64.800003, "t": 383.5379899999999, "r": 391.75464, "b": 391.86301, "coord_origin": "TOPLEFT"}, "confidence": 0.9337190985679626, "cells": [{"id": 23, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "bbox": {"l": 64.800003, "t": 383.5379899999999, "r": 391.75464, "b": 391.86301, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 70.800003, "t": 400.51827999999995, "r": 536.76337, "b": 715.862068, "coord_origin": "TOPLEFT"}, "confidence": 0.9899572730064392, "cells": [{"id": 24, "text": "User action", "bbox": {"l": 70.800301, "t": 400.51827999999995, "r": 119.78551, "b": 408.84329, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*JOBCTL", "bbox": {"l": 424.93805, "t": 447.52255, "r": 433.26297000000005, "b": 487.01999, 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This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa\u2019s job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665539860725403, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 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Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 2. Roles and separation of duties"}], "body": [{"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9789126515388489, "cells": [{"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa\u2019s job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665539860725403, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 64.800003, "t": 383.5379899999999, "r": 391.75464, "b": 391.86301, "coord_origin": "TOPLEFT"}, "confidence": 0.9337190985679626, "cells": [{"id": 23, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "bbox": {"l": 64.800003, "t": 383.5379899999999, "r": 391.75464, "b": 391.86301, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority"}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 70.800003, "t": 400.51827999999995, "r": 536.76337, "b": 715.862068, "coord_origin": "TOPLEFT"}, "confidence": 0.9899572730064392, "cells": [{"id": 24, "text": "User action", "bbox": {"l": 70.800301, "t": 400.51827999999995, "r": 119.78551, "b": 408.84329, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*JOBCTL", "bbox": {"l": 424.93805, "t": 447.52255, "r": 433.26297000000005, "b": 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Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 2. Roles and separation of duties"}]}}, {"page_no": 9, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "15", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The SQL ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 179.58179, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "CREATE PERMISSION", "bbox": {"l": 179.57977, "t": 71.65845000000002, "r": 264.47879, "b": 80.48302999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " statement that is shown in Figure 3-1 is used to define and ", "bbox": {"l": 264.53955, "t": 71.50903000000005, "r": 528.73059, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "initially enable or disable the row access rules.", "bbox": {"l": 136.79956, "t": 83.50885000000017, "r": 341.71762, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Figure 3-1 CREATE PERMISSION SQL statement", "bbox": {"l": 136.8, "t": 414.138, "r": 341.97659, "b": 422.46301, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Column mask", "bbox": {"l": 136.8, "t": 439.94399999999996, "r": 215.37601, "b": 451.04401, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "A column mask is a database object that manifests a column value access control rule for a ", "bbox": {"l": 136.8, "t": 455.08871000000005, "r": 542.76648, "b": 464.3017, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "specific column in a specific table. 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For example, a teller can see only the last four digits of a tax ", "bbox": {"l": 136.8, "t": 479.08835, "r": 538.80927, "b": 488.30133, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "identification number.", "bbox": {"l": 136.8, "t": 491.08817, "r": 231.20888, "b": 500.30115, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "CREATE PERMISSION", "bbox": {"l": 148.1337, "t": 139.67969000000005, "r": 246.7961, "b": 149.50982999999997, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "<", "bbox": {"l": 251.86685, "t": 139.67969000000005, "r": 257.58578, "b": 149.49834999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "permission name", "bbox": {"l": 257.59152, "t": 139.67969000000005, "r": 336.99741, "b": 149.50982999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ">", "bbox": {"l": 337.01233, "t": 139.67969000000005, "r": 342.73126, "b": 149.49834999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Names the row permission for row access control", "bbox": {"l": 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For example, a teller can see only the last four digits of a tax identification number."}], "headers": [{"label": "page_footer", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9071078896522522, "cells": [{"id": 1, "text": "15", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "15"}, {"label": "page_footer", "id": 2, "page_no": 9, "cluster": {"id": 2, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9526246786117554, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}]}}, {"page_no": 10, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "P1", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Proc1:", "bbox": {"l": 148.4301, "t": 473.58524, "r": 174.05859, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Owner = JOE", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "SET OPTION USRPRF=*OWNER", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "USER = ALICE", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "CURRENT USER = JOE", "bbox": {"l": 148.4301, "t": 533.30984, "r": 234.57686999999999, "b": 541.82059, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 566.15842, "r": 191.70256, "b": 574.66917, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 578.10333, "r": 232.56117, "b": 586.61409, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "caption", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.7875164747238159, "cells": [{"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253707528114319, "cells": [{"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 535.65082, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 20, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445975422859192, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748277068138123, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 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"USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}, "confidence": 0.6404176950454712, "cells": [{"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}, "confidence": 0.5759296417236328, "cells": [{"id": 43, "text": "P1", 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When no adopted  authority is present, this has the same value as USER.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "SYSTEM_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The authorization ID that initiated the connection.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445975422859192, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE\u2019s authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748277068138123, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "picture", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 135.64837646484375, "t": 384.1736755371094, "r": 301.2367248535156, "b": 594.7566528320312, "coord_origin": "TOPLEFT"}, "confidence": 0.7221462726593018, "cells": [], "children": [{"id": 15, "label": "text", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}, "confidence": 0.7616674900054932, "cells": [{"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 40, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}, "confidence": 0.6404176950454712, "cells": [{"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}, "confidence": 0.5759296417236328, "cells": [{"id": 43, "text": "P1", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Proc1:", "bbox": {"l": 148.4301, "t": 473.58524, "r": 174.05859, "b": 482.09598, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 45, "text": "Owner = JOE", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 46, "text": "SET OPTION USRPRF=*OWNER", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": 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[{"id": 50, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 578.10333, "r": 232.56117, "b": 586.61409, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274529814720154, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"id": 21, 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global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9130509495735168, "cells": [{"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "19"}, {"label": "page_footer", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9557498693466187, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}], "body": [{"label": "caption", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.7875164747238159, "cells": [{"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-1 summarizes these special registers and their values."}, {"label": "caption", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253707528114319, "cells": [{"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-1 Special registers and their corresponding values"}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 535.65082, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 20, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl"], "num_rows": 4, "num_cols": 2, "table_cells": [{"bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Special register", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Corresponding value", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER or SESSION_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread excluding adopted authority.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "CURRENT_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread including adopted authority. When no adopted  authority is present, this has the same value as USER.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "SYSTEM_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The authorization ID that initiated the connection.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445975422859192, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE\u2019s authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748277068138123, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "picture", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 135.64837646484375, "t": 384.1736755371094, "r": 301.2367248535156, "b": 594.7566528320312, "coord_origin": "TOPLEFT"}, "confidence": 0.7221462726593018, "cells": [], "children": [{"id": 15, "label": "text", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}, "confidence": 0.7616674900054932, "cells": [{"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 40, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}, "confidence": 0.6404176950454712, "cells": [{"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}, "confidence": 0.5759296417236328, "cells": [{"id": 43, "text": "P1", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Proc1:", "bbox": {"l": 148.4301, "t": 473.58524, "r": 174.05859, "b": 482.09598, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 45, "text": "Owner = JOE", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 46, "text": "SET OPTION USRPRF=*OWNER", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 47, "text": "USER = ALICE", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 148.4301, "t": 533.30984, "r": 234.57686999999999, "b": 541.82059, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 48, "text": "CURRENT USER = JOE", "bbox": {"l": 148.4301, "t": 533.30984, "r": 234.57686999999999, "b": 541.82059, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 36, "label": "text", "bbox": {"l": 138.476, "t": 566.15842, "r": 191.70256, "b": 574.66917, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 49, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 566.15842, "r": 191.70256, "b": 574.66917, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 138.476, "t": 578.10333, "r": 232.56117, "b": 586.61409, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 50, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 578.10333, "r": 232.56117, "b": 586.61409, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274529814720154, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.2.2 Built-in global variables"}, {"label": "text", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 672.92153, "coord_origin": "TOPLEFT"}, "confidence": 0.9696778059005737, "cells": [{"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}], "headers": [{"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9130509495735168, "cells": [{"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "19"}, {"label": "page_footer", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9557498693466187, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Description", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CLIENT_HOST", "bbox": {"l": 70.800003, "t": 129.49834999999996, "r": 132.7209, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "VARCHAR(255)", "bbox": {"l": 202.89029, "t": 129.49834999999996, "r": 267.07651, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Host name of the current client as returned by the system", "bbox": {"l": 281.84732, "t": 129.49834999999996, "r": 510.17548, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "CLIENT_IPADDR", "bbox": {"l": 70.800018, "t": 148.51806999999997, "r": 140.66522, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "VARCHAR(128)", "bbox": {"l": 202.87231, "t": 148.51806999999997, "r": 267.07739, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "IP address of the current client as returned by the system", "bbox": {"l": 281.84549, "t": 148.51806999999997, "r": 509.60583, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "CLIENT_PORT ", "bbox": {"l": 70.800018, "t": 167.53778, "r": 134.98264, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "INTEGER", "bbox": {"l": 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These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. 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It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237534403800964, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338628649711609, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514462947845459, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512302875518799, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.706649661064148, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The 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"start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Type of the currently running routine", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.52872, "r": 547.23474, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237534403800964, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338628649711609, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514462947845459, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512302875518799, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.706649661064148, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"label": "page_footer", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9187921285629272, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "20"}, {"label": "page_footer", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9505080580711365, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8370980620384216, "cells": [{"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 lists the nine built-in global variables."}, {"label": "caption", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9132355451583862, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 527.59222, "b": 289.86227, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 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"coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "ROUTINE_SCHEMA", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 202.79312, "t": 243.55724999999995, "r": 267.09274, "b": 251.8822, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(128)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 281.87164, "t": 243.55724999999995, "r": 464.26022, "b": 251.8822, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Schema name of the currently running routine", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 70.800018, "t": 262.51757999999995, "r": 188.43991, "b": 270.84253, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "ROUTINE_SPECIFIC_NAME", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 202.84441, "t": 262.51757999999995, "r": 267.03693, "b": 270.84253, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(128)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 281.80682, "t": 262.51757999999995, "r": 430.40045, "b": 270.84253, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Name of the currently running routine", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 70.800034, "t": 281.53726, "r": 139.43135, "b": 289.86227, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "ROUTINE_TYPE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 202.74635, "t": 281.53726, "r": 239.28996000000004, "b": 289.86227, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "CHAR(1)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 281.79065, "t": 281.53726, "r": 425.09131, "b": 289.86227, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Type of the currently running routine", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.52872, "r": 547.23474, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237534403800964, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338628649711609, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514462947845459, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512302875518799, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.706649661064148, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}], "headers": [{"label": "page_footer", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9187921285629272, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "20"}, {"label": "page_footer", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9505080580711365, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606333613395691, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897271037101746, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785220146179199, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678944706916809, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467734694480896, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705167412757874, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469641447067261, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003496885299683, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.848820149898529, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411225080490112, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.910578727722168, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606333613395691, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897271037101746, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785220146179199, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678944706916809, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467734694480896, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705167412757874, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469641447067261, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003496885299683, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.848820149898529, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411225080490112, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.910578727722168, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "27"}, {"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}], "body": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606333613395691, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897271037101746, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785220146179199, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678944706916809, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467734694480896, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705167412757874, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469641447067261, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003496885299683, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.848820149898529, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411225080490112, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}], "headers": [{"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.910578727722168, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "27"}, {"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}]}}, {"page_no": 13, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 63.80192184448242, "t": 95.38238525390625, "r": 547.11474609375, "b": 170.0321044921875, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570101976394653, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.600240170955658, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985856056213379, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449256062507629, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.9049987792969, "r": 530.0478515625, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801458716392517, "cells": [], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.80192184448242, "t": 95.38238525390625, "r": 547.11474609375, "b": 170.0321044921875, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570101976394653, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.600240170955658, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985856056213379, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Column Access Control (masks)"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "*/"}, {"label": "text", "id": 16, "page_no": 13, "cluster": {"id": 16, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 17, "page_no": 13, "cluster": {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 18, "page_no": 13, "cluster": {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449256062507629, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.9049987792969, "r": 530.0478515625, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801458716392517, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.80192184448242, "t": 95.38238525390625, "r": 547.11474609375, "b": 170.0321044921875, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570101976394653, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.600240170955658, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985856056213379, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Column Access Control (masks)"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "*/"}, {"label": "text", "id": 16, "page_no": 13, "cluster": {"id": 16, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 17, "page_no": 13, "cluster": {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 18, "page_no": 13, "cluster": {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449256062507629, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.9049987792969, "r": 530.0478515625, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801458716392517, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}], "headers": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 14, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 4. 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Implementing Row and Column Access Control: Banking example ", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}]}}, {"page_no": 15, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632163763046265, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8994000554084778, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632163763046265, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}, {"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8994000554084778, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "124"}, {"label": "page_footer", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632163763046265, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}], "headers": [{"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8994000554084778, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "124"}, {"label": "page_footer", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 16, "size": {"width": 612.0, "height": 792.0}, "cells": [], "predictions": {"layout": {"clusters": [{"id": 0, "label": "form", "bbox": {"l": 0.4932013750076294, "t": 0.0, "r": 610.2305297851562, "b": 791.654541015625, "coord_origin": "TOPLEFT"}, "confidence": 0.6497195363044739, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "form", "id": 0, "page_no": 16, "cluster": {"id": 0, "label": "form", "bbox": {"l": 0.4932013750076294, "t": 0.0, "r": 610.2305297851562, "b": 791.654541015625, "coord_origin": "TOPLEFT"}, "confidence": 0.6497195363044739, "cells": [], "children": []}, "text": null}], "body": [{"label": "form", "id": 0, "page_no": 16, "cluster": {"id": 0, "label": "form", "bbox": {"l": 0.4932013750076294, "t": 0.0, "r": 610.2305297851562, "b": 791.654541015625, "coord_origin": "TOPLEFT"}, "confidence": 0.6497195363044739, "cells": [], "children": []}, "text": null}], "headers": []}}, {"page_no": 17, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "fi", "bbox": {"l": 558.11987, "t": 45.468689999999924, "r": 565.46039, "b": 54.68169999999998, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "REDP-5110-00", "bbox": {"l": 171.0, "t": 631.338, "r": 231.88769999999997, "b": 639.66301, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "INTERNATIONAL ", "bbox": {"l": 467.3399999999999, "t": 247.71831999999995, "r": 559.80933, "b": 260.16052, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "TECHNICAL", "bbox": {"l": 467.3399999999999, "t": 261.75842, "r": 529.50208, "b": 274.20061999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "SUPPORT", "bbox": {"l": 467.3399999999999, "t": 275.73839999999996, "r": 518.93317, "b": 288.1806, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ORGANIZATION", "bbox": {"l": 467.3399999999999, "t": 289.71841, "r": 550.7475, "b": 302.16061, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "BUILDING TECHNICAL ", "bbox": {"l": 467.3399999999999, "t": 351.79199, "r": 571.70758, "b": 362.47198, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "INFORMATION BASED ON ", "bbox": {"l": 467.3399999999999, "t": 363.79199, "r": 587.38916, "b": 374.47198, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "PRACTICAL EXPERIENCE", "bbox": {"l": 467.3399999999999, "t": 375.79199, "r": 582.5556, "b": 386.47198, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "IBM Redbooks are developed ", "bbox": {"l": 467.3399999999999, "t": 399.8602900000001, "r": 587.46674, "b": 409.63251, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "by the IBM International ", "bbox": {"l": 467.3399999999999, "t": 410.90067, "r": 566.34229, "b": 420.67285, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Technical Support ", "bbox": {"l": 467.3399999999999, "t": 421.88068, "r": 543.20404, "b": 431.65289, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Organization. Experts from ", "bbox": {"l": 467.3399999999999, "t": 432.8606899999999, "r": 577.76697, "b": 442.63287, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "IBM, Customers and Partners ", "bbox": {"l": 467.3399999999999, "t": 443.90106, "r": 587.40948, "b": 453.67328, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "from around the world create ", "bbox": {"l": 467.3399999999999, "t": 454.88107, "r": 587.52051, "b": 464.65326, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "timely technical information ", "bbox": {"l": 467.3399999999999, "t": 465.86108, "r": 582.67505, "b": 475.6333, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "based on realistic scenarios. ", "bbox": {"l": 467.3399999999999, "t": 476.90146, "r": 585.46722, "b": 486.67365, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Specific recommendations ", "bbox": {"l": 467.3399999999999, "t": 487.88147, "r": 577.70874, "b": 497.65369, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "are provided to help you ", "bbox": {"l": 467.3399999999999, "t": 498.86148, "r": 568.03546, "b": 508.63367, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implement IT solutions more ", "bbox": {"l": 467.3399999999999, "t": 509.90186, "r": 585.44525, "b": 519.67407, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "effectively in your ", "bbox": {"l": 467.3399999999999, "t": 520.8818699999999, "r": 541.4967, "b": 530.65405, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "environment.", "bbox": {"l": 467.3399999999999, "t": 531.8618799999999, "r": 520.64893, "b": 541.63406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "For more information:", "bbox": {"l": 467.3399999999999, "t": 578.83191, "r": 570.948, "b": 589.5119, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ibm.com", "bbox": {"l": 467.3399999999999, "t": 590.83191, "r": 508.59961, "b": 601.5119, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "/redbooks", "bbox": {"l": 508.56000000000006, "t": 590.83191, "r": 552.74518, "b": 601.5119, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Redpaper", "bbox": {"l": 474.60001, "t": 164.05658000000005, "r": 580.88989, "b": 188.94097999999997, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "\u2122", "bbox": {"l": 582.53992, "t": 172.32714999999996, "r": 592.13989, "b": 181.20714999999996, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Row and Column Access Control ", "bbox": {"l": 27.0, "t": 73.63799999999992, "r": 447.36002, "b": 103.00800000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Support in IBM DB2 for i", "bbox": {"l": 27.0, "t": 113.76000999999997, "r": 314.43002, "b": 140.46002, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Implement roles and ", "bbox": {"l": 26.700001, "t": 242.17200000000003, "r": 127.4436, "b": 252.85199, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "separation of duties", "bbox": {"l": 26.700001, "t": 256.15198, "r": 121.6608, "b": 266.83196999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Leverage row ", "bbox": {"l": 26.700001, "t": 284.17197, "r": 93.970795, "b": 294.85196, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "permissions on the ", "bbox": {"l": 26.700001, "t": 298.15198000000004, "r": 120.28319999999998, "b": 308.83197, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "database", "bbox": {"l": 26.700001, "t": 312.19199000000003, "r": 70.413605, "b": 322.87198, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Protect columns by ", "bbox": {"l": 26.700001, "t": 340.15198000000004, "r": 121.44960000000002, "b": 350.83197, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "defining column ", "bbox": {"l": 26.700001, "t": 354.19199000000003, "r": 106.5696, "b": 364.87198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "masks", "bbox": {"l": 26.700001, "t": 368.1720000000001, "r": 58.194, "b": 378.85199, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "This IBM Redpaper publication provides information about the IBM i 7.2 ", "bbox": {"l": 152.94, "t": 242.72857999999997, "r": 413.99057, "b": 251.59295999999995, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "feature of IBM DB2 for i Row and Column Access Control (RCAC). 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2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3.6.2", "bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 173.32271, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8541, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "23", "bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "3.6.3", "bbox": {"l": 151.19717, "t": 643.66034, "r": 173.32227, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.85353, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "24", "bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "3.6.4", "bbox": {"l": 151.19717, "t": 656.14021, "r": 173.35289, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89182, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "25", "bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "3.6.5", "bbox": {"l": 151.19717, "t": 668.62009, "r": 173.35289, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Defining and creating column masks", "bbox": {"l": 178.89182, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.98996, "t": 668.62009, "r": 530.54413, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "26", "bbox": {"l": 536.08301, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "3.6.6", "bbox": {"l": 151.19717, "t": 681.15973, "r": 173.38359, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.93019, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "28", "bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3.6.7", "bbox": {"l": 151.19717, "t": 693.63961, "r": 173.32332, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Demonstrating data access with RCAC", "bbox": {"l": 178.85486, "t": 693.63961, "r": 350.80011, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 356.33163, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "29", "bbox": {"l": 536.10663, "t": 693.63961, "r": 547.16968, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "3.6.8", "bbox": {"l": 151.19717, "t": 706.119492, "r": 173.44592, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00812, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "32", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "document_index", "bbox": {"l": 136.15110778808594, "t": 132.0302734375, "r": 547.5270385742188, "b": 715.6527709960938, "coord_origin": "TOPLEFT"}, "confidence": 0.9803490042686462, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3.6.2", "bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 173.32271, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8541, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "23", "bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "3.6.3", "bbox": {"l": 151.19717, "t": 643.66034, "r": 173.32227, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.85353, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "24", "bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "3.6.4", "bbox": {"l": 151.19717, "t": 656.14021, "r": 173.35289, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89182, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "25", "bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "3.6.5", "bbox": {"l": 151.19717, "t": 668.62009, "r": 173.35289, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Defining and creating column masks", "bbox": {"l": 178.89182, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.98996, "t": 668.62009, "r": 530.54413, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "26", "bbox": {"l": 536.08301, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "3.6.6", "bbox": {"l": 151.19717, "t": 681.15973, "r": 173.38359, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.93019, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "28", "bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3.6.7", "bbox": {"l": 151.19717, "t": 693.63961, "r": 173.32332, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Demonstrating data access with RCAC", "bbox": {"l": 178.85486, "t": 693.63961, "r": 350.80011, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 356.33163, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "29", "bbox": {"l": 536.10663, "t": 693.63961, "r": 547.16968, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "3.6.8", "bbox": {"l": 151.19717, "t": 706.119492, "r": 173.44592, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00812, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "32", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "children": [{"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 195.39685, "t": 145.12847999999997, 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with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00812, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 128, "label": "text", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 127, "text": "32", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 3, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.892595112323761, "cells": [{"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9473134279251099, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.15110778808594, "t": 132.0302734375, "r": 547.5270385742188, "b": 715.6527709960938, "coord_origin": "TOPLEFT"}, "confidence": 0.9803490042686462, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 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. . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 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built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 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"row_section": false}, {"bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, 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1, "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0882, "t": 643.66034, "r": 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"row_section": false}, {"bbox": {"l": 151.19717, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.5 Defining and creating column masks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.98996, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 0, 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false, "row_section": false}]}}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Contents"}, {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.15110778808594, "t": 132.0302734375, "r": 547.5270385742188, "b": 715.6527709960938, "coord_origin": "TOPLEFT"}, "confidence": 0.9803490042686462, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3.6.2", "bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 173.32271, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8541, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "23", "bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "3.6.3", "bbox": {"l": 151.19717, "t": 643.66034, "r": 173.32227, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.85353, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "24", 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"b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 30, "end_row_offset_idx": 31, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 30, "end_row_offset_idx": 31, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "19", "column_header": false, "row_header": false, 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"end_col_offset_idx": 1, "text": "3.4 Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 32, "end_row_offset_idx": 33, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "21", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79701, "t": 593.62131, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC 3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, 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1, "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "24", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "25", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.5 Defining and creating column masks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.98996, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "28", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10663, "t": 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false, "row_section": false}]}, {"label": "page_footer", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.892595112323761, "cells": [{"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "iii"}, {"label": "page_footer", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9473134279251099, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}], "body": [{"label": "section_header", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Contents"}, {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.15110778808594, "t": 132.0302734375, "r": 547.5270385742188, "b": 715.6527709960938, "coord_origin": "TOPLEFT"}, "confidence": 0.9803490042686462, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 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. . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Preface", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 542.91888, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "DB2fi for i Row and Column Access Control (RCAC). 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A solid background in IBM i object level security, DB2 for i relational ", "bbox": {"l": 136.79987, "t": 238.60712, "r": 521.25488, "b": 247.82012999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "database concepts, and SQL is assumed.", "bbox": {"l": 136.79987, "t": 250.60693000000003, "r": 321.69434, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. 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He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. 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His areas of expertise are database technology, ", "bbox": {"l": 263.3996, "t": 647.62454, "r": 524.77386, "b": 656.83754, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "performance, and data warehousing. Hernando can be ", "bbox": {"l": 263.3996, "t": 659.62434, "r": 508.27124, "b": 668.83735, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "contacted at ", "bbox": {"l": 263.3996, "t": 671.62415, "r": 320.63568, "b": 680.83716, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "hbedoya@us.ibm.com", "bbox": {"l": 320.63971, "t": 671.77356, "r": 410.57852, "b": 680.54832, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": ".", "bbox": {"l": 410.5795, "t": 671.62415, "r": 413.34839, "b": 680.83716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master\u2019s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"label": "page_footer", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9040942788124084, "cells": [{"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "xi"}, {"label": "page_footer", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469243884086609, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}], "body": [{"label": "section_header", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9174709916114807, "cells": [{"id": 2, "text": "Preface", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preface"}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79984, "t": 132.64862000000005, "r": 547.30823, "b": 201.86072000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9869155883789062, "cells": [{"id": 3, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 542.91888, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "bbox": {"l": 136.79984, "t": 144.64844000000005, "r": 526.65509, "b": 153.86145, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "bbox": {"l": 136.79984, "t": 156.64824999999996, "r": 536.82135, "b": 165.86127, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "bbox": {"l": 136.79987, "t": 168.64806999999996, "r": 544.67975, "b": 177.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "of defining, creating, and implementing the row permissions and column masks in a relational ", "bbox": {"l": 136.79987, "t": 180.64788999999996, "r": 547.30823, "b": 189.86090000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "database environment.", "bbox": {"l": 136.79987, "t": 192.64770999999996, "r": 238.32117, "b": 201.86072000000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79987, "t": 214.60748, "r": 546.4657, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9872201681137085, "cells": [{"id": 9, "text": "This paper is intended for database engineers, data-centric application developers, and ", "bbox": {"l": 136.79987, "t": 214.60748, "r": 524.18518, "b": 223.82050000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security officers who want to design and implement RCAC as a part of their data control and ", "bbox": {"l": 136.79987, "t": 226.6073, "r": 546.4657, "b": 235.82030999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "bbox": {"l": 136.79987, "t": 238.60712, "r": 521.25488, "b": 247.82012999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "database concepts, and SQL is assumed.", "bbox": {"l": 136.79987, "t": 250.60693000000003, "r": 321.69434, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"label": "section_header", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}, "confidence": 0.9255505204200745, "cells": [{"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Authors"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 341.84152, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318943977356, "cells": [{"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"label": "picture", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 142.52883911132812, "t": 375.0449523925781, "r": 251.47850036621094, "b": 503.20648193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9862572550773621, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 263.39957, "t": 375.64877, "r": 541.25079, "b": 516.85974, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "bbox": {"l": 263.3996, "t": 459.64749, "r": 528.91016, "b": 468.86047, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "numerous technical roles, including independent software ", "bbox": {"l": 263.3996, "t": 471.64731, "r": 520.24207, "b": 480.86029, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "vendors technical support on a broad range of IBM ", "bbox": {"l": 263.3996, "t": 483.64713, "r": 490.6967200000001, "b": 492.86011, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "technologies and products, and supporting customers in the ", "bbox": {"l": 263.3996, "t": 495.64694, "r": 530.95514, "b": 504.85992, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "IBM Executive Briefing Center and IBM Project Office.", "bbox": {"l": 263.3996, "t": 507.64676, "r": 501.62973, "b": 516.85974, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"label": "picture", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 145.41445922851562, "t": 527.2447509765625, "r": 252.08840942382812, "b": 635.383056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.987165629863739, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 263.3996, "t": 527.62653, "r": 541.27374, "b": 680.83716, "coord_origin": "TOPLEFT"}, "confidence": 0.9823779463768005, "cells": [{"id": 29, "text": "Hernando Bedoya", "bbox": {"l": 263.3996, "t": 527.62653, "r": 348.38229, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": " is a Senior IT Specialist at STG Lab ", "bbox": {"l": 348.41916, "t": 527.62653, "r": 512.3429, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Services and Training in Rochester, Minnesota. He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. His areas of expertise are database technology, ", "bbox": {"l": 263.3996, "t": 647.62454, "r": 524.77386, "b": 656.83754, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "performance, and data warehousing. Hernando can be ", "bbox": {"l": 263.3996, "t": 659.62434, "r": 508.27124, "b": 668.83735, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "contacted at ", "bbox": {"l": 263.3996, "t": 671.62415, "r": 320.63568, "b": 680.83716, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "hbedoya@us.ibm.com", "bbox": {"l": 320.63971, "t": 671.77356, "r": 410.57852, "b": 680.54832, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": ".", "bbox": {"l": 410.5795, "t": 671.62415, "r": 413.34839, "b": 680.83716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master\u2019s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}], "headers": [{"label": "page_footer", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9040942788124084, "cells": [{"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "xi"}, {"label": "page_footer", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469243884086609, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "1", "bbox": {"l": 541.67987, "t": 754.848721, "r": 547.21765, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Chapter 1.", "bbox": {"l": 81.0, "t": 268.54272000000003, "r": 115.13253, "b": 274.98071000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Securing and protecting IBM DB2 ", "bbox": {"l": 136.8, "t": 254.88635, "r": 547.30475, "b": 278.91785000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "data", "bbox": {"l": 136.8, "t": 285.84671, "r": 190.29802, "b": 309.8782, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "bbox": {"l": 136.8, "t": 348.70871, "r": 542.25665, "b": 357.92169, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 ", "bbox": {"l": 136.80096, "t": 360.70853, "r": 544.96643, "b": 369.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "bbox": {"l": 136.79965, "t": 372.70853, "r": 529.53839, "b": 381.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ ", "bbox": {"l": 136.79965, "t": 384.7083400000001, "r": 535.32874, "b": 393.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "bbox": {"l": 136.80026, "t": 396.70853, "r": 521.64374, "b": 405.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "bbox": {"l": 136.80026, "t": 408.7083400000001, "r": 547.13135, "b": 417.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "bbox": {"l": 136.80023, "t": 420.70816, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 525.06482, "b": 451.9407, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "bbox": {"l": 136.80025, "t": 454.72754000000003, "r": 518.26825, "b": 463.94052, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "industries, securing the data is required by law and subject to audits. 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All rights reserved."}], "body": [{"label": "picture", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "picture", "bbox": {"l": 32.05510711669922, "t": 70.42633819580078, "r": 239.62696838378906, "b": 238.0409698486328, "coord_origin": "TOPLEFT"}, "confidence": 0.7604207992553711, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 500.39999, "t": 93.16870000000006, "r": 522.61774, "b": 130.13171, "coord_origin": "TOPLEFT"}, "confidence": 0.7054201364517212, "cells": [{"id": 24, "text": "1", "bbox": {"l": 500.39999, "t": 93.16870000000006, "r": 522.61774, "b": 130.13171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}, {"label": "section_header", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 136.8, "t": 254.88635, "r": 547.30475, "b": 309.8782, "coord_origin": "TOPLEFT"}, "confidence": 0.9377050399780273, "cells": [{"id": 3, "text": "Securing and protecting IBM DB2 ", "bbox": {"l": 136.8, "t": 254.88635, "r": 547.30475, "b": 278.91785000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "data", "bbox": {"l": 136.8, "t": 285.84671, "r": 190.29802, "b": 309.8782, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Securing and protecting IBM DB2 data"}, {"label": "text", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "text", "bbox": {"l": 81.0, "t": 268.54272000000003, "r": 115.13253, "b": 274.98071000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 2, "text": "Chapter 1.", "bbox": {"l": 81.0, "t": 268.54272000000003, "r": 115.13253, "b": 274.98071000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 1."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79965, "t": 348.70871, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}, "confidence": 0.9868757724761963, "cells": [{"id": 5, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "bbox": {"l": 136.8, "t": 348.70871, "r": 542.25665, "b": 357.92169, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 ", "bbox": {"l": 136.80096, "t": 360.70853, "r": 544.96643, "b": 369.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "bbox": {"l": 136.79965, "t": 372.70853, "r": 529.53839, "b": 381.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ ", "bbox": {"l": 136.79965, "t": 384.7083400000001, "r": 535.32874, "b": 393.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "bbox": {"l": 136.80026, "t": 396.70853, "r": 521.64374, "b": 405.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "bbox": {"l": 136.80026, "t": 408.7083400000001, "r": 547.13135, "b": 417.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "bbox": {"l": 136.80023, "t": 420.70816, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 527.2063, "b": 487.94016, "coord_origin": "TOPLEFT"}, "confidence": 0.9865864515304565, "cells": [{"id": 12, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 525.06482, "b": 451.9407, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "bbox": {"l": 136.80025, "t": 454.72754000000003, "r": 518.26825, "b": 463.94052, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "bbox": {"l": 136.80025, "t": 466.72736, "r": 527.2063, "b": 475.94034, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "longer an option; it is a requirement.", "bbox": {"l": 136.80025, "t": 478.72717, "r": 296.31067, "b": 487.94016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80025, "t": 500.68698, "r": 547.15515, "b": 521.89978, "coord_origin": "TOPLEFT"}, "confidence": 0.9734498858451843, "cells": [{"id": 16, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "bbox": {"l": 136.80025, "t": 500.68698, "r": 547.15515, "b": 509.89996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "are covered in this chapter:", "bbox": {"l": 136.80025, "t": 512.6868, "r": 257.28036, "b": 521.89978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This chapter describes how you can secure and protect data in DB2 for i. 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. 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", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837489128112793, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621999263763428, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. 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Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> First, and most important, is the definition of a company\u2019s security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077319264411926, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. 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Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837489128112793, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621999263763428, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company\u2019s most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today\u2019s connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}, {"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889443874359131, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "section_header", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1 Security fundamentals"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9663435220718384, "cells": [{"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"label": "list_item", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.12487999999996, "r": 547.16425, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> First, and most important, is the definition of a company\u2019s security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077319264411926, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837489128112793, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621999263763428, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company\u2019s most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today\u2019s connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}], "headers": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889443874359131, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. 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Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858148097991943, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.9757603406906128, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838991165161133, "cells": [], "children": [{"id": 9, "label": "text", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.949161946773529, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. 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However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.9757603406906128, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"label": "picture", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838991165161133, "cells": [], "children": [{"id": 9, "label": "text", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1-2 Existing row and column controls"}, {"label": "page_footer", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_footer", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.949161946773529, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. 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Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"label": "section_header", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.3.1 Existing row and column control"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858148097991943, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. 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However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.9757603406906128, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], 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"cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_footer", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.949161946773529, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "2.2", "bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "VARCHAR(30)", "bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "DENIED: The user profile is not allowed to use the function.", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "USER_TYPE", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "VARCHAR(5)", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Type of user profile:", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "GROUP: The user profile is a group.", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.969738245010376, "cells": [{"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}, "confidence": 0.9551491737365723, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410472512245178, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417492747306824, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.95621657371521, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682287573814392, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056942462921143, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636377096176147, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737266302108765, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197836518287659, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 135.5250701904297, "t": 289.7249450683594, "r": 545.87060546875, "b": 442.0505065917969, "coord_origin": "TOPLEFT"}, "confidence": 0.9851696491241455, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "VARCHAR(30)", "bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 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"b": 106.70172000000014, "coord_origin": "TOPLEFT"}, "confidence": 0.9551491737365723, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410472512245178, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417492747306824, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.95621657371521, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682287573814392, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056942462921143, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636377096176147, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737266302108765, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197836518287659, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.5250701904297, "t": 289.7249450683594, "r": 545.87060546875, "b": 442.0505065917969, "coord_origin": "TOPLEFT"}, "confidence": 0.9851696491241455, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, 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270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 45, "label": "text", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 64, "text": "DENIED: The user profile is not allowed to use the function.", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 63, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 46, "label": "text", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, 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GLYPH<SM590000> DENIED: The user profile is not allowed to use the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_TYPE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(5)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.43161, "t": 405.55865, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Type of user profile: GLYPH<SM590000> USER: The user profile is a user. GLYPH<SM590000> GROUP: The user profile is a group.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647642970085144, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 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Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9166075587272644, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9529877305030823, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.969738245010376, "cells": [{"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.6 Change Function Usage CL command"}, {"label": "text", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}, "confidence": 0.9551491737365723, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410472512245178, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417492747306824, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.95621657371521, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682287573814392, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056942462921143, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636377096176147, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737266302108765, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197836518287659, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.5250701904297, "t": 289.7249450683594, "r": 545.87060546875, "b": 442.0505065917969, "coord_origin": "TOPLEFT"}, "confidence": 0.9851696491241455, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, 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{"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": 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293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 45, "label": "text", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 64, "text": "DENIED: The user profile is not allowed to use the function.", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 63, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 46, "label": "text", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, 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2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(30)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "ID of the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_NAME", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(10)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 353.88333, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Name of the user profile that has a usage setting for this  function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USAGE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(7)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.41626, "t": 364.51862, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Usage setting: GLYPH<SM590000> ALLOWED: The user profile is allowed to use the function. GLYPH<SM590000> DENIED: The user profile is not allowed to use the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_TYPE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(5)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.43161, "t": 405.55865, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Type of user profile: GLYPH<SM590000> USER: The user profile is a user. GLYPH<SM590000> GROUP: The user profile is a group.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647642970085144, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 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178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 0.5631598830223083, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}, "confidence": 0.0, 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"bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.54872, "r": 547.22345, "b": 709.760956, "coord_origin": "TOPLEFT"}, "confidence": 0.9853105545043945, "cells": [{"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}], "headers": [{"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9166075587272644, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9529877305030823, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 2. Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "11", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "bbox": {"l": 64.800003, "t": 383.5379899999999, "r": 391.75464, "b": 391.86301, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "User action", "bbox": {"l": 70.800301, "t": 400.51827999999995, "r": 119.78551, "b": 408.84329, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*JOBCTL", "bbox": {"l": 424.93805, "t": 447.52255, "r": 433.26297000000005, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "QIBM_DB_SECADM", "bbox": {"l": 450.13806, "t": 401.6000700000001, "r": 458.46298, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "QIBM_DB_SQLADM", "bbox": {"l": 475.93835000000007, "t": 401.53442, "r": 484.26327999999995, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "QIBM_DB_SYSMON", "bbox": {"l": 501.13837, "t": 401.6145, "r": 509.46329, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "No Authority", "bbox": {"l": 526.39862, "t": 432.79944, "r": 534.72357, "b": 487.02005, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "SET CURRENT DEGREE", "bbox": {"l": 70.800003, "t": 498.69299, "r": 151.6794, "b": 506.66699, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " (SQL statement)", "bbox": {"l": 151.6803, "t": 498.55798, "r": 220.15681000000004, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "X", "bbox": {"l": 429.0, "t": 498.55798, "r": 435.00299000000007, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 480.00031, "t": 498.55798, "r": 486.0033, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "CHGQRYA", "bbox": {"l": 70.800018, "t": 517.65329, "r": 102.23972, "b": 525.62729, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": " command targeting a different user\u2019s job", "bbox": {"l": 102.23972, "t": 517.51828, "r": 264.5538, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "X", "bbox": {"l": 429.00003, "t": 517.51828, "r": 435.00302000000005, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "X", "bbox": {"l": 480.00034, "t": 517.51828, "r": 486.00333, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "STRDBMON", "bbox": {"l": 70.800049, "t": 536.67299, "r": 106.73975, "b": 544.64699, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": " or ", "bbox": {"l": 106.73975, "t": 536.5379800000001, "r": 119.77895, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "ENDDBMON", "bbox": {"l": 119.69975000000001, "t": 536.67299, "r": 155.69974, "b": 544.64699, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": " commands targeting a different user\u2019s job", "bbox": {"l": 155.69974, "t": 536.5379800000001, "r": 322.50574, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "X", "bbox": {"l": 429.00003, "t": 536.5379800000001, "r": 435.00302000000005, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "X", "bbox": {"l": 480.00034, "t": 536.5379800000001, "r": 486.00333, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "STRDBMON", "bbox": {"l": 70.800049, "t": 555.69269, "r": 106.73975, "b": 563.66669, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": " or ", "bbox": {"l": 106.73975, "t": 555.55768, "r": 119.77895, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ENDDBMON", "bbox": {"l": 119.69975000000001, "t": 555.69269, "r": 155.69974, "b": 563.66669, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": " commands targeting a job that matches the current user", "bbox": {"l": 155.69974, "t": 555.55768, "r": 381.02185, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "X", "bbox": {"l": 429.00003, "t": 555.55768, "r": 435.00302000000005, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "X", "bbox": {"l": 480.00034, "t": 555.55768, "r": 486.00333, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "X", "bbox": {"l": 505.26061999999996, "t": 555.55768, "r": 511.26361, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "X", "bbox": {"l": 530.76031, "t": 555.55768, "r": 536.76331, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "QUSRJOBI() API format 900 or System i Navigator\u2019s SQL Details for Job", "bbox": {"l": 70.800049, "t": 574.51797, "r": 359.51736, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "X", "bbox": {"l": 429.0000600000001, "t": 574.51797, "r": 435.00305000000003, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "X", "bbox": {"l": 480.00037, "t": 574.51797, "r": 486.00335999999993, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "X", "bbox": {"l": 505.2606799999999, "t": 574.51797, "r": 511.26367, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Visual Explain within Run SQL scripts", "bbox": {"l": 70.800079, "t": 593.5376699999999, "r": 220.75178999999997, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "X", "bbox": {"l": 429.0000600000001, "t": 593.5376699999999, "r": 435.00305000000003, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "X", "bbox": {"l": 480.00037, "t": 593.5376699999999, "r": 486.00335999999993, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "X", "bbox": {"l": 505.2606799999999, "t": 593.5376699999999, "r": 511.26367, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "X", "bbox": {"l": 530.76038, "t": 593.5376699999999, "r": 536.76337, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "Visual Explain outside of Run SQL scripts", "bbox": {"l": 70.800079, "t": 612.55737, "r": 236.6548, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "X", "bbox": {"l": 429.0000600000001, "t": 612.55737, "r": 435.00305000000003, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "X", "bbox": {"l": 480.00037, "t": 612.55737, "r": 486.00335999999993, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "ANALYZE PLAN CACHE procedure", "bbox": {"l": 70.800079, "t": 631.51767, "r": 213.12968, "b": 639.84268, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "X", "bbox": {"l": 429.0000600000001, "t": 631.51767, "r": 435.00305000000003, "b": 639.84268, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "X", "bbox": {"l": 480.00037, "t": 631.51767, "r": 486.00335999999993, "b": 639.84268, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "DUMP PLAN CACHE procedure", "bbox": {"l": 70.800079, "t": 650.53737, "r": 199.87808, "b": 658.86238, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "X", "bbox": {"l": 429.0000600000001, "t": 650.53737, "r": 435.00305000000003, "b": 658.86238, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "X", "bbox": {"l": 480.00037, "t": 650.53737, "r": 486.00335999999993, "b": 658.86238, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "MODIFY PLAN CACHE procedure", "bbox": {"l": 70.800079, "t": 669.55708, "r": 208.36777, "b": 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"coord_origin": "TOPLEFT"}}, {"id": 77, "text": "X", "bbox": {"l": 429.0000600000001, "t": 707.537071, "r": 435.00305000000003, "b": 715.862068, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "X", "bbox": {"l": 480.00037, "t": 707.537071, "r": 486.00335999999993, "b": 715.862068, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 2, "label": "text", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9789126515388489, "cells": [{"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665539860725403, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i 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", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 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Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 2. Roles and separation of duties"}], "body": [{"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9789126515388489, "cells": [{"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa\u2019s job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665539860725403, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 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Row and Column Access Control"}]}}, {"page_no": 10, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "P1", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Proc1:", "bbox": {"l": 148.4301, "t": 473.58524, "r": 174.05859, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Owner = JOE", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "SET OPTION USRPRF=*OWNER", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "USER = ALICE", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "CURRENT USER = JOE", "bbox": {"l": 148.4301, "t": 533.30984, "r": 234.57686999999999, "b": 541.82059, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 566.15842, "r": 191.70256, "b": 574.66917, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 578.10333, "r": 232.56117, "b": 586.61409, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "caption", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.7875164747238159, "cells": [{"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253707528114319, "cells": [{"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 134.5463104248047, "t": 103.41889190673828, "r": 542.0460205078125, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 20, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445975422859192, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. 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When no adopted  authority is present, this has the same value as USER.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "SYSTEM_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The authorization ID that initiated the connection.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445975422859192, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE\u2019s authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748277068138123, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "picture", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 135.64837646484375, "t": 384.1736755371094, "r": 301.2367248535156, "b": 594.7566528320312, "coord_origin": "TOPLEFT"}, "confidence": 0.7221462726593018, "cells": [], "children": [{"id": 15, "label": "text", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}, "confidence": 0.7616674900054932, "cells": [{"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 40, "text": 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global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9130509495735168, "cells": [{"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "19"}, {"label": "page_footer", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9557498693466187, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}], "body": [{"label": "caption", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.7875164747238159, "cells": [{"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-1 summarizes these special registers and their values."}, {"label": "caption", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253707528114319, "cells": [{"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-1 Special registers and their corresponding values"}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 134.5463104248047, "t": 103.41889190673828, "r": 542.0460205078125, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. 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When no adopted  authority is present, this has the same value as USER.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "SYSTEM_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The authorization ID that initiated the connection.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445975422859192, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE\u2019s authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748277068138123, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "picture", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 135.64837646484375, "t": 384.1736755371094, "r": 301.2367248535156, "b": 594.7566528320312, "coord_origin": "TOPLEFT"}, "confidence": 0.7221462726593018, "cells": [], "children": [{"id": 15, "label": "text", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}, "confidence": 0.7616674900054932, "cells": [{"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 40, "text": 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[{"id": 50, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 578.10333, "r": 232.56117, "b": 586.61409, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274529814720154, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"id": 21, 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global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}], "headers": [{"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9130509495735168, "cells": [{"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "19"}, {"label": "page_footer", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9557498693466187, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Description", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CLIENT_HOST", "bbox": {"l": 70.800003, "t": 129.49834999999996, "r": 132.7209, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "VARCHAR(255)", "bbox": {"l": 202.89029, "t": 129.49834999999996, "r": 267.07651, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Host name of the current client as returned by the system", "bbox": {"l": 281.84732, "t": 129.49834999999996, "r": 510.17548, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "CLIENT_IPADDR", "bbox": {"l": 70.800018, "t": 148.51806999999997, "r": 140.66522, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "VARCHAR(128)", "bbox": {"l": 202.87231, "t": 148.51806999999997, "r": 267.07739, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "IP address of the current client as returned by the system", "bbox": {"l": 281.84549, "t": 148.51806999999997, "r": 509.60583, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "CLIENT_PORT ", "bbox": {"l": 70.800018, "t": 167.53778, "r": 134.98264, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "INTEGER", "bbox": {"l": 202.90294, "t": 167.53778, "r": 242.80084, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Port used by the current client to communicate with the server", "bbox": {"l": 281.79785, "t": 167.53778, "r": 527.59222, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "PACKAGE_NAME", "bbox": {"l": 70.800018, "t": 186.5575, "r": 143.50925, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "VARCHAR(128)", "bbox": {"l": 202.80576, "t": 186.5575, "r": 267.06937, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Name of the currently running package", "bbox": {"l": 281.85187, "t": 186.5575, "r": 436.57259999999997, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "PACKAGE_SCHEMA", "bbox": {"l": 70.800018, "t": 205.51782000000003, "r": 156.01654, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "VARCHAR(128)", "bbox": {"l": 202.83545, "t": 205.51782000000003, "r": 267.08646, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Schema name of the currently running package", "bbox": {"l": 281.87076, "t": 205.51782000000003, "r": 470.44678, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "PACKAGE_VERSION", "bbox": {"l": 70.800018, "t": 224.53754000000004, "r": 157.89932, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "VARCHAR(64)", "bbox": {"l": 202.72472, "t": 224.53754000000004, "r": 261.98254, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Version identifier of the currently running package", "bbox": {"l": 281.74924, "t": 224.53754000000004, "r": 478.8438100000001, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ROUTINE_SCHEMA", "bbox": {"l": 70.800018, "t": 243.55724999999995, "r": 154.41992, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "VARCHAR(128)", "bbox": {"l": 202.79312, "t": 243.55724999999995, "r": 267.09274, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Schema name of the currently running routine", "bbox": {"l": 281.87164, "t": 243.55724999999995, "r": 464.26022, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ROUTINE_SPECIFIC_NAME", "bbox": {"l": 70.800018, "t": 262.51757999999995, "r": 188.43991, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "VARCHAR(128)", "bbox": {"l": 202.84441, "t": 262.51757999999995, "r": 267.03693, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Name of the currently running routine", "bbox": {"l": 281.80682, "t": 262.51757999999995, "r": 430.40045, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "ROUTINE_TYPE", "bbox": {"l": 70.800034, "t": 281.53726, "r": 139.43135, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "CHAR(1)", "bbox": {"l": 202.74635, "t": 281.53726, "r": 239.28996000000004, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Type of the currently running routine", "bbox": {"l": 281.79065, "t": 281.53726, "r": 425.09131, "b": 289.86227, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 11, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8370980620384216, "cells": [{"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9132355451583862, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 63.55636978149414, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Description", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CLIENT_HOST", "bbox": {"l": 70.800003, "t": 129.49834999999996, "r": 132.7209, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "VARCHAR(255)", "bbox": {"l": 202.89029, "t": 129.49834999999996, "r": 267.07651, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Host name of the current client as returned by the system", "bbox": {"l": 281.84732, "t": 129.49834999999996, "r": 510.17548, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "CLIENT_IPADDR", "bbox": {"l": 70.800018, "t": 148.51806999999997, "r": 140.66522, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "VARCHAR(128)", "bbox": {"l": 202.87231, "t": 148.51806999999997, "r": 267.07739, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "IP address of the current client as returned by the system", "bbox": {"l": 281.84549, "t": 148.51806999999997, "r": 509.60583, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "CLIENT_PORT ", "bbox": {"l": 70.800018, "t": 167.53778, "r": 134.98264, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "INTEGER", "bbox": {"l": 202.90294, "t": 167.53778, "r": 242.80084, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Port used by the current client to communicate with the server", "bbox": {"l": 281.79785, "t": 167.53778, "r": 527.59222, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "PACKAGE_NAME", "bbox": {"l": 70.800018, "t": 186.5575, "r": 143.50925, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "VARCHAR(128)", "bbox": {"l": 202.80576, "t": 186.5575, "r": 267.06937, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Name of the currently running package", "bbox": {"l": 281.85187, "t": 186.5575, "r": 436.57259999999997, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "PACKAGE_SCHEMA", "bbox": {"l": 70.800018, "t": 205.51782000000003, "r": 156.01654, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "VARCHAR(128)", "bbox": {"l": 202.83545, "t": 205.51782000000003, "r": 267.08646, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Schema name of the currently running package", "bbox": {"l": 281.87076, "t": 205.51782000000003, "r": 470.44678, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "PACKAGE_VERSION", "bbox": {"l": 70.800018, "t": 224.53754000000004, "r": 157.89932, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "VARCHAR(64)", "bbox": {"l": 202.72472, "t": 224.53754000000004, "r": 261.98254, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Version identifier of the currently running package", "bbox": {"l": 281.74924, "t": 224.53754000000004, "r": 478.8438100000001, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ROUTINE_SCHEMA", "bbox": {"l": 70.800018, "t": 243.55724999999995, "r": 154.41992, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "VARCHAR(128)", "bbox": {"l": 202.79312, "t": 243.55724999999995, "r": 267.09274, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Schema name of the currently running routine", "bbox": {"l": 281.87164, "t": 243.55724999999995, "r": 464.26022, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ROUTINE_SPECIFIC_NAME", "bbox": {"l": 70.800018, "t": 262.51757999999995, "r": 188.43991, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "VARCHAR(128)", "bbox": {"l": 202.84441, "t": 262.51757999999995, "r": 267.03693, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Name of the currently running routine", "bbox": {"l": 281.80682, "t": 262.51757999999995, "r": 430.40045, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "ROUTINE_TYPE", "bbox": {"l": 70.800034, "t": 281.53726, "r": 139.43135, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "CHAR(1)", "bbox": {"l": 202.74635, "t": 281.53726, "r": 239.28996000000004, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Type of the currently running routine", "bbox": {"l": 281.79065, "t": 281.53726, "r": 425.09131, "b": 289.86227, "coord_origin": "TOPLEFT"}}], "children": [{"id": 14, "label": "text", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 31, "text": "Description", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}}], 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129.49834999999996, "r": 510.17548, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 70.800018, "t": 148.51806999999997, "r": 140.66522, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 35, "text": "CLIENT_IPADDR", "bbox": {"l": 70.800018, "t": 148.51806999999997, "r": 140.66522, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 202.87231, "t": 148.51806999999997, "r": 267.07739, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 36, "text": "VARCHAR(128)", "bbox": {"l": 202.87231, "t": 148.51806999999997, "r": 267.07739, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 281.84549, "t": 148.51806999999997, "r": 509.60583, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 37, 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Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237534403800964, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338628649711609, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514462947845459, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512302875518799, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.706649661064148, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9187921285629272, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9505080580711365, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 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"coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237534403800964, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338628649711609, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514462947845459, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512302875518799, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.706649661064148, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"label": "page_footer", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9187921285629272, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "20"}, {"label": "page_footer", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9505080580711365, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8370980620384216, "cells": [{"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 lists the nine built-in global variables."}, {"label": "caption", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9132355451583862, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 63.55636978149414, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, 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Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237534403800964, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338628649711609, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514462947845459, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512302875518799, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.706649661064148, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}], "headers": [{"label": "page_footer", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9187921285629272, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "20"}, {"label": "page_footer", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9505080580711365, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606333613395691, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897271037101746, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785220146179199, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678944706916809, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467734694480896, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705167412757874, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469641447067261, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003496885299683, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.848820149898529, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411225080490112, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.910578727722168, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606333613395691, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897271037101746, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785220146179199, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678944706916809, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467734694480896, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705167412757874, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469641447067261, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003496885299683, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.848820149898529, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411225080490112, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.910578727722168, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "27"}, {"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}], "body": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606333613395691, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897271037101746, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785220146179199, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678944706916809, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467734694480896, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705167412757874, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469641447067261, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003496885299683, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.848820149898529, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411225080490112, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}], "headers": [{"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.910578727722168, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "27"}, {"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}]}}, {"page_no": 13, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 63.80192184448242, "t": 95.38238525390625, "r": 547.11474609375, "b": 170.0321044921875, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570101976394653, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.600240170955658, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985856056213379, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449256062507629, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.9049987792969, "r": 530.0478515625, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801458716392517, "cells": [], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.80192184448242, "t": 95.38238525390625, "r": 547.11474609375, "b": 170.0321044921875, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570101976394653, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.600240170955658, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985856056213379, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Column Access Control (masks)"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "*/"}, {"label": "text", "id": 16, "page_no": 13, "cluster": {"id": 16, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 17, "page_no": 13, "cluster": {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 18, "page_no": 13, "cluster": {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449256062507629, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.9049987792969, "r": 530.0478515625, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801458716392517, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.80192184448242, "t": 95.38238525390625, "r": 547.11474609375, "b": 170.0321044921875, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570101976394653, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.600240170955658, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985856056213379, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Column Access Control (masks)"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "*/"}, {"label": "text", "id": 16, "page_no": 13, "cluster": {"id": 16, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 17, "page_no": 13, "cluster": {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 18, "page_no": 13, "cluster": {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.0, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449256062507629, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.9049987792969, "r": 530.0478515625, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801458716392517, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}], "headers": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 14, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "77", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "2.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 145.19554, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "bbox": {"l": 147.9942, "t": 71.50903000000005, "r": 513.35919, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "enabled. It is clear that the implementation of the SQL statement is more complex ", "bbox": {"l": 151.19975, "t": 83.50885000000017, "r": 514.04858, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "because the row permission rule becomes part of the ", "bbox": {"l": 151.19975, "t": 95.50867000000005, "r": 389.64822, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHERE", "bbox": {"l": 389.57941, "t": 95.65808000000015, "r": 414.53918, "b": 104.48266999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": " clause.", "bbox": {"l": 414.59991, "t": 95.50867000000005, "r": 448.8892200000001, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Figure 4-68 Visual Explain with RCAC enabled", "bbox": {"l": 136.8, "t": 480.55798, "r": 327.09329, "b": 488.883, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "3.", "bbox": {"l": 136.8, "t": 506.56863, "r": 145.17432, "b": 515.78162, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "bbox": {"l": 147.96574, "t": 506.56863, "r": 543.63715, "b": 515.78162, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "bbox": {"l": 151.20016, "t": 518.56845, "r": 547.23944, "b": 527.78143, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "enabled. The index being advised is for the ORDER BY clause.", "bbox": {"l": 151.20016, "t": 530.5682400000001, "r": 430.28333, "b": 539.78125, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Figure 4-69 Index advice with no RCAC", "bbox": {"l": 64.800003, "t": 667.5179, "r": 227.10149, "b": 675.8429, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 3, "label": "list_item", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 514.04858, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9761855006217957, "cells": [{"id": 2, "text": "2.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 145.19554, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "bbox": {"l": 147.9942, "t": 71.50903000000005, "r": 513.35919, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "enabled. 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Figure 4-69 shows the index advice for the SQL statement without RCAC ", "bbox": {"l": 151.20016, "t": 518.56845, "r": 547.23944, "b": 527.78143, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "enabled. 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Implementing Row and Column Access Control: Banking example ", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "list_item", "id": 3, "page_no": 14, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 514.04858, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9761855006217957, "cells": [{"id": 2, "text": "2.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 145.19554, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "bbox": {"l": 147.9942, "t": 71.50903000000005, "r": 513.35919, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "enabled. 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It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause."}, {"label": "picture", "id": 0, "page_no": 14, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 136.5016632080078, "t": 119.24909210205078, "r": 545.4508666992188, "b": 477.54119873046875, "coord_origin": "TOPLEFT"}, "confidence": 0.9864527583122253, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 5, "page_no": 14, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 136.8, "t": 480.55798, "r": 327.09329, "b": 488.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9529654383659363, "cells": [{"id": 8, "text": "Figure 4-68 Visual Explain with RCAC enabled", "bbox": {"l": 136.8, "t": 480.55798, "r": 327.09329, "b": 488.883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4-68 Visual Explain with RCAC enabled"}, {"label": "list_item", "id": 2, "page_no": 14, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.8, "t": 506.56863, "r": 547.23944, "b": 539.78125, "coord_origin": "TOPLEFT"}, "confidence": 0.9766737818717957, "cells": [{"id": 9, "text": "3.", "bbox": {"l": 136.8, "t": 506.56863, "r": 145.17432, "b": 515.78162, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "bbox": {"l": 147.96574, "t": 506.56863, "r": 543.63715, "b": 515.78162, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "bbox": {"l": 151.20016, "t": 518.56845, "r": 547.23944, "b": 527.78143, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "enabled. The index being advised is for the ORDER BY clause.", "bbox": {"l": 151.20016, "t": 530.5682400000001, "r": 430.28333, "b": 539.78125, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause."}, {"label": "picture", "id": 1, "page_no": 14, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 64.27847290039062, "t": 553.5814819335938, "r": 506.39263916015625, "b": 664.0870971679688, "coord_origin": "TOPLEFT"}, "confidence": 0.9797717928886414, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 6, "page_no": 14, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 64.800003, "t": 667.5179, "r": 227.10149, "b": 675.8429, "coord_origin": "TOPLEFT"}, "confidence": 0.9514288306236267, "cells": [{"id": 13, "text": "Figure 4-69 Index advice with no RCAC", "bbox": {"l": 64.800003, "t": 667.5179, "r": 227.10149, "b": 675.8429, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4-69 Index advice with no RCAC"}, {"label": "page_footer", "id": 7, "page_no": 14, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9056528806686401, "cells": [{"id": 1, "text": "77", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "77"}, {"label": "page_footer", "id": 4, "page_no": 14, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9557602405548096, "cells": [{"id": 0, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}], "body": [{"label": "list_item", "id": 3, "page_no": 14, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 514.04858, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9761855006217957, "cells": [{"id": 2, "text": "2.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 145.19554, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "bbox": {"l": 147.9942, "t": 71.50903000000005, "r": 513.35919, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "enabled. It is clear that the implementation of the SQL statement is more complex ", "bbox": {"l": 151.19975, "t": 83.50885000000017, "r": 514.04858, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "because the row permission rule becomes part of the ", "bbox": {"l": 151.19975, "t": 95.50867000000005, "r": 389.64822, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHERE", "bbox": {"l": 389.57941, "t": 95.65808000000015, "r": 414.53918, "b": 104.48266999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": " clause.", "bbox": {"l": 414.59991, "t": 95.50867000000005, "r": 448.8892200000001, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause."}, {"label": "picture", "id": 0, "page_no": 14, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 136.5016632080078, "t": 119.24909210205078, "r": 545.4508666992188, "b": 477.54119873046875, "coord_origin": "TOPLEFT"}, "confidence": 0.9864527583122253, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 5, "page_no": 14, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 136.8, "t": 480.55798, "r": 327.09329, "b": 488.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9529654383659363, "cells": [{"id": 8, "text": "Figure 4-68 Visual Explain with RCAC enabled", "bbox": {"l": 136.8, "t": 480.55798, "r": 327.09329, "b": 488.883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4-68 Visual Explain with RCAC enabled"}, {"label": "list_item", "id": 2, "page_no": 14, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.8, "t": 506.56863, "r": 547.23944, "b": 539.78125, "coord_origin": "TOPLEFT"}, "confidence": 0.9766737818717957, "cells": [{"id": 9, "text": "3.", "bbox": {"l": 136.8, "t": 506.56863, "r": 145.17432, "b": 515.78162, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "bbox": {"l": 147.96574, "t": 506.56863, "r": 543.63715, "b": 515.78162, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "bbox": {"l": 151.20016, "t": 518.56845, "r": 547.23944, "b": 527.78143, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "enabled. The index being advised is for the ORDER BY clause.", "bbox": {"l": 151.20016, "t": 530.5682400000001, "r": 430.28333, "b": 539.78125, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause."}, {"label": "picture", "id": 1, "page_no": 14, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 64.27847290039062, "t": 553.5814819335938, "r": 506.39263916015625, "b": 664.0870971679688, "coord_origin": "TOPLEFT"}, "confidence": 0.9797717928886414, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 6, "page_no": 14, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 64.800003, "t": 667.5179, "r": 227.10149, "b": 675.8429, "coord_origin": "TOPLEFT"}, "confidence": 0.9514288306236267, "cells": [{"id": 13, "text": "Figure 4-69 Index advice with no RCAC", "bbox": {"l": 64.800003, "t": 667.5179, "r": 227.10149, "b": 675.8429, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4-69 Index advice with no RCAC"}], "headers": [{"label": "page_footer", "id": 7, "page_no": 14, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9056528806686401, "cells": [{"id": 1, "text": "77", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "77"}, {"label": "page_footer", "id": 4, "page_no": 14, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9557602405548096, "cells": [{"id": 0, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}]}}, {"page_no": 15, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632163763046265, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8994000554084778, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632163763046265, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}, {"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8994000554084778, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "124"}, {"label": "page_footer", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}], "body": [{"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632163763046265, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . 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